It kinda skips over how large mainstream journals, with their restrictive and often arbitrary standards, have contributed to this. Most will refuse to publish replications, negative studies, or anything they deem unimportant, even if the study was conducted correctly.
So much of this started with the rise of the peer-review journal cartel, beginning with Pergamon Press in 1951 (coincidentally founded by Ghislaine Maxwell's father). "Peer review" didn't exist before then, science papers and discussion was published openly, and scientists focused on quality not quantity.
I'm not sure that the system was ever that near to perfection: for example, John Maddox of Nature didn't like the advent of pre-publication peer review, but that presumably had something to do with it limiting his discretion to approve and desk-reject whatever he wanted. But in any case it (like other aspects of the cozy interwar and then wartime scientific world) could surely never have survived the huge scaling-up that had already begun in the post-war era and created the pressure to switch to pre-publication peer reivew in the first place.
"The crises that face science [from the ending of exponential growth in science funding after the Cold War period] are not limited to jobs and research funds. Those are bad enough, but they are just the beginning. Under stress from those problems, other parts of the scientific enterprise have started showing signs of distress. One of the most essential is the matter of honesty and ethical behavior among scientists.
The public and the scientific community have both been shocked in recent years by an increasing number of cases of fraud committed by scientists. There is little doubt that the perpetrators in these cases felt themselves under intense pressure to compete for scarce resources, even by cheating if necessary. As the pressure increases, this kind of dishonesty is almost sure to become more common.
Other kinds of dishonesty will also become more common. For example, peer review, one of the crucial pillars of the whole edifice, is in critical danger. Peer review is used by scientific journals to decide what papers to publish, and by granting agencies such as the National Science Foundation to decide what research to support. Journals in most cases, and agencies in some cases operate by sending manuscripts or research proposals to referees who are recognized experts on the scientific issues in question, and whose identity will not be revealed to the authors of the papers or proposals. Obviously, good decisions on what research should be supported and what results should be published are crucial to the proper functioning of science.
Peer review is usually quite a good way to identify valid science. Of course, a referee will occasionally fail to appreciate a truly visionary or revolutionary idea, but by and large, peer review works pretty well so long as scientific validity is the only issue at stake. However, it is not at all suited to arbitrate an intense competition for research funds or for editorial space in prestigious journals. There are many reasons for this, not the least being the fact that the referees have an obvious conflict of interest, since they are themselves competitors for the same resources. This point seems to be another one of those relativistic anomalies, obvious to any outside observer, but invisible to those of us who are falling into the black hole. It would take impossibly high ethical standards for referees to avoid taking advantage of their privileged anonymity to advance their own interests, but as time goes on, more and more referees have their ethical standards eroded as a consequence of having themselves been victimized by unfair reviews when they were authors. Peer review is thus one among many examples of practices that were well suited to the time of exponential expansion, but will become increasingly dysfunctional in the difficult future we face.
We must find a radically different social structure to organize research and education in science after The Big Crunch. That is not meant to be an exhortation. It is meant simply to be a statement of a fact known to be true with mathematical certainty, if science is to survive at all. The new structure will come about by evolution rather than design, because, for one thing, neither I nor anyone else has the faintest idea of what it will turn out to be, and for another, even if we did know where we are going to end up, we scientists have never been very good at guiding our own destiny. Only this much is sure: the era of exponential expansion will be replaced by an era of constraint. Because it will be unplanned, the transition is likely to be messy and painful for the participants. In fact, as we have seen, it already is. Ignoring the pain for the moment, however, I would like to look ahead and speculate on some conditions that must be met if science is to have a future as well as a past. ..."
The paper may have a point in that the internet makes possible a certain scale of deception via paper mills and brokers and such -- but the motivation to use the internet that way comes from the growing financial pressures that Dr. Goodstein identified.
Fun fact, he almost got the worldwide console rights to Tetris back in the 80s, and tried going to Soviet officials to get those rights. To the point he's the antagonist of a recent "Tetris" movie that came out.
What is currently called "peer review" didn't exist back then, back then the meaning of "peer review" was just the back and forth happening in the open academic literature. Note the inevitable lack of finality in the original concept of peer review, a discussion in the scientific community could go on for 100's of years before being finally resolved. The current concept of "peer review" is closer to the concept of a delegation of some opaque ministry of truth composed of some opaquely selected experts (who often truly intend well) to settle in a short duration the finality.
Some measurements or experiments or questions to be settled can be very actionable and provide highly accurate results, others require much longer gathering of data to draw a clear picture.
The modern concept of "peer review" tries to sell the idea of almost immediate finality, like an economic transaction. In reality it is selling just the illusion, and creating lots of victims ranging from truth, individuals, departments institutions, or even entire fields (think of the replication crisis in psychology) along with any patients or others they treat.
I believe by saying it is coincidental they are saying there is probably no relevance, just an interesting piece of trivia, why put out this interesting piece of trivia? Because maybe someone will be able to make an argument of relevance.
Ghislaine's father (Robert Maxwell) was also a terrible person but for different reasons.
Robert Maxwell was a crook, he used pension funds (supposed to be ring-fenced for the benefit of the pensioners) to prop up his companies, so, after his slightly mysterious death it was discovered that basically there's no money to pay people who've been assured of a pension when they retire.
He was also very litigious. If you said he was a crook when he was alive you'd better hope you can prove it and that you have funding to stay in the fight until you do. So this means the sort of people who call out crooks were especially unhappy about Robert Maxwell because he was a crook and he might sue you if you pointed it out.
I imagine it's the interesting peculiarity that the same people seem to crop up over and over and over again. Six degrees of Kevin Bacon or something, except it's like one or two degrees. As George Carlin said, "it's a big club, and you ain't in it"
For example Donald Barr (father of twice-former US Attorney General Bill Barr) hiring college-dropout Jeffrey Epstein whilst headmaster at the elite Dalton School
Additional fun facts about Donald Barr: he served in US intelligence during WWII, and wrote a sci-fi book featuring child sex slaves
Also the Epstein-Barr virus causes Mono, the clone of .NET, which was created by Bill Gates, known associate of Epstein, whose father was president of the Washington State Bar Association. And you know who else works in Washington? Join the dots, people.
We call people who make connections like these "conspiracy theorists," until they're right, at which point we call them "right". And somewhere in between, if they manage to get a job, we call them "Simpsons writers."
If you want to know more about the history of Pergamon Press there's a great Behind the Bastards episode on Robert Maxwell (Ghislaine Maxwell's father) - who himself was a scumbag in a variety of ways that were entirely distinct from Ghislaine Maxwell's brand of scumbaggery - that covers this. Might even be a multipart episode - it's a while since I've listened to it, but I have a feeling it's at least a two parter.
"Coincidental" means random, with no causal connection being explicitly claimed. It just means that two things share some characteristic (such as being relatives.) The thing that is coincidental is that the person who founded the company being discussed is also the father of another person who current events have brought into prominence.
It's why you would say something like "more than coincidental" if you were trying to make some causal claim, like one thing causing the other, or both things coming from the same cause.
So, "What is coincidental about that?" is a weird question. It reads as a rhetorical claim of a causal connection through asking for a denial or a disproof of one.
what is the relevance to the discussion about journals and peer review is my main question.
if i randomly mentioned that your name appears to be an alternate spelling of a 3-band active EQ guitar pedal, coincidentally sharing all of the letters except one, in my reply to you, most people would be confused. that is how i felt when randomly reading "Ghislaine Maxwell" in this context of journals and peer review.
I know a PhD professor doing post doc or something, and he accepted a scientific study just because it was published in Nature.
He didn't look at methodology or data.
From that point forward, I have never really respected Academia. They seem like bottom floor scientists who never truly understood the scientific method.
It helped that a year later Ivys had their cheating scandals, fake data, and academia wide replication crisis.
When I read something in a textbook I blindly believe it, depending on the broader context and the textbook in question. Is that a bad thing?
People are constantly filtering everything based on heuristics. The important thing is to know how deep to look in any given situation. Hopefully the person you're referring to is proficient at that.
Keep in mind that research scientists need to keep abreast of far more developments than any human could possibly study in detail. Also that 50% of people are below average at their job.
There is a vast difference between a student reading from a textbook and a researcher / scientist reading studies and/or papers.
As a student you are to be directed* in your reading by an expert in the field of study that you are learning from. In many higher level courses a professor will assign multiple textbooks and assign reading from only particular chapters of those textbooks specifically because they have vetted those chapters for accuracy and alignment with their curriculum.
As a researcher and scientist a very large portion of your job is verifying and then integrating the research of others into your domain knowledge. The whole purpose of replicating studies is to look critically at the methodology of another scientist and try as hard as you can to prove them wrong. If you fail to prove them wrong and can produce the same results as them, they have done Good Science.
A textbook is the product of scientists and researchers Doing Science and publishing their results, other scientists and researchers verifying via replication, and then one of those scientists or researchers who is an expert in the field doing their best to compile their knowledge on the domain into a factually accurate and (relatively) easy to understand summary of the collective research performed in a specific domain.
The fact is that people make mistakes, and the job of a professor (who is an expert in a given field) is to identify what errors have made it through the various checks mentioned above and into circulation, often times making subjective judgement calls about what is 'factual enough' for the level of the class they are teaching, and leverage that to build a curriculum that is sound and helps elevate other individuals to the level of knowledge required to contribute to the ongoing scientific journey.
In short, it's not a bad thing if you're learning a subject by yourself for your own purposes and are not contributing to scientific advancement or working as an educator in higher-education.
* You can self-study, but to become an expert while doing so requires extremely keen discernment to be able to root out the common misconceptions that proliferate in any given field. In a blue-collar field this would be akin to picking up 'bad technique' by watching YouTube videos published by another self-taught tradesman; it's not always obvious when it happens.
> There is a vast difference between a student reading from a textbook and a researcher / scientist reading studies and/or papers.
Not really. Both are learning new things. Neither has the time or access to resources to replicate even a small fraction of things learned. Neither will ever make direct use of the vast majority of things learned.
Thus both depend on a cooperative model where trust is given to third parties to whom knowledge aggregation is outsourced. In that sense a textbook and prestigious peer reviewed journals serve the same purpose.
Papers in any journal (even or especially Nature, depending on your prior) should have a significantly larger degree of skepticism shown towards them than statements in reputable textbooks (which also should not be taken as complete gospel). Papers are a 'hey, we did a thing once, here's what we think it means' from a source that is very strongly motivated to do or find something novel or interesting, even if you trust that there is no fraud they are not something to approach uncritically.
> If you fail to prove them wrong and can produce the same results as them, they have done Good Science.
Not really in my humble opinion. Sure, the Popperian vibe is kind of fundamental, but the whole truncation into binary-valued true/false categories seldom makes sense with many (or even most?) problems for which probabilities, effect sizes, and related things matter more.
And if you fail to replicate a study, they may have still done Good Science. With replications, it should not be about Bad Science and Good Science but about the cumulation of evidence (or a lack thereof). That's what meta-analyses are about.
When we talk about Bad Science, it is about the industrial-scale fraud the article is talking about. No one should waste time replicating, citing, or reading that.
This is a good point. It is not humanly possible to verify every claim you read from every source.
Ideally, you should independently verify claims that appear to be particularly consequential or particularly questionable on the surface. But at some point you have to rely on heuristics like chain of trust (it was peer reviewed, it was published in a reputable textbook), or you will never make forward progress on anything.
> When I read something in a textbook I blindly believe it, depending on the broader context and the textbook in question. Is that a bad thing?
It is if what you read is factually incorrect, yes.
For example, I have read in a textbook that the tongue has very specific regions for taste. This is patently false.
> Keep in mind that research scientists need to keep abreast of far more developments than any human could possibly study in detail. Also that 50% of people are below average at their job.
So, we should probably just discount half of what we read from research scientists as "bad at their job" and not pay much attention to it? Which half? Why are you defending corruption?
The problem is that you can't just verify everything yourself. You likely have your own deadlines, and/or you want to do something more interesting than replicating statistical tests from a random paper.
Yes? What is exactly funny here? This is literally how the civilization works. I'm paid to do my work, and I pay others to do their work.
Do you grow your own food and sew your own clothes? Also, did you personally etch the microprocessor that runs your computer? The division of labor inherently means trusting others. So when I buy a bag of M4 screws, I'm not going to measure each screw with a micrometer, and I'm not taking X-ray spectra to verify their material composition.
The academic world also used to trust large publishers to take care to actually review papers. It appears that this trust is now misplaced. But I don't think it was somehow stupid.
>The problem is that you can't just verify everything yourself..
But one should be free to reject (in the sense that they should be free not to depend on it, or take decisions based on it) things that they did not themselves verify. But today if you do that, you become "anti-science"...and in-fact the people who wants others to "believe" science is anti-science..
Most of the times you don't "accept" results. You have to build something on them, like an extension or a similar version on other field. So usually the first step is try to understand the cryptic published version and do a reproduction or something as close as possible.
The exact reproductions is never published, because journals don't accept them, but if you add a few tweaks here and there you have a nice seed for an article to publish somewhere.
(I may "accept" an article in a field I don't care, but you probably should not thrust my opinion in fields I don't care.)
Academia has problems, like everywhere else. But that seems like a big extrapolation from just one professor.
Fake data—you can only get that type of scandal when people are checking the data. I’d be more skeptical of communities that never have that kind of scandal.
In this case, the problem is a bit easier to identify and solve. Specifically, the Q-rating and publish or perish system is at fault. That can be fixed, or at least improved. Maybe we should be doing that instead of denying the obvious problems.
tl;dr He is the bridge that uncomfortably links Biden's former Secretary of State, Antony Blinken, to Jeffrey Epstein and Mossad. Hence, *gestures at the last couple of weeks and years*. Dude was just, like, Fraud Central, apparently.
Plenty will publish it, but those are not as highly regarded by the community. It's not a problem of journals. It's not hard to start your own journal by teaming up with other academics. In machine learning, ICLR is such a venue for example. The problem is much deeper and more fundamental. You want to publish alongside groundbreaking novel research. Researcher's own ears perk up when they hear about something new. They invite colleagues to talk about their novel discoveries not to describe all their null results and successful replications of known results. Funding agencies want research with novelty and impact. They want to write reports to the higher ups and the politicians and the donors that document the innovations that their funding brought. The media will republish press releases that have cool new results.
To have research happening, you need someone saying "I want to give money to this researcher". There is an endless queue of people lining up who are ready to take this money and do something with it. The person with money (govt or private) has to use some heuristics to pick. One way is to say "I trust this one, I don't care too much what the project is, I'm sure this person will do something that makes sense". But that is dependent on a track record.
Do you want issues of Nature and cell to be replication studies? As a reader even from within the field, im not interested in browsing through negative studies. It'll be great if I can look them up when needed but im not looking forward to email ToC alerts filled with them.
Also who's funding you for replication work? Do you know the pressure you have in tenure track to have a consistent thesis on what you work on?
Literally every single know that designs academia is tuned to not incentivize what you complain about. Its not just journals being picky.
Also the people committing fraud aren't ones who will say "gosh I will replicate things now!" Replicating work is far more difficult than a lot of original work.
> Do you want issues of Nature and cell to be replication studies?
Of course I do! Not all of course, and taking (subjectively measured) impact into account. "We tried to replicate the study published in the same journal 3 years ago using a larger sample size and failed to achieve similar results..." OR "after successfully replicating the study we can confirm the therapeutic mechanism proposed by X actually works" - these are extremely important results that are takin into account in meta studies and e.g. form the base of policies worldwide.
Honestly even if they didn't publish the whole paper, if there was just a page that was a table of all the replication studies that were done recently, that would be pretty cool.
Maybe nature and cell and a few other journals should be exceptions: they should be the place that the most advanced scientists publish interesting ideas early for the consumption by their competitors. At that level of science, all the competitors can reproduce each other's experiments if necessary; the real value is expanding the knowledge of what seems possible quickly.
(I am not seriously proposing this, but it's interesting to think about distinguishing between the very small amount of truly innovative discovery versus the very long tail of more routine methods development and filling out gaps in knowledge)
In my own experience I was unable to publish a few works because I was unable to outperform a "competitor" (technically we're all on the same side, right?). So I dig more and more into their work and really try to replicate their work. I can't! Emailing the authors I get no further and only more questions. I submit the papers anyways, adding a section about replication efforts. You guessed it, rejected. With explicit comments from reviewers about lack of impact due to "competitor's" results.
Is an experience I've found a lot of colleagues share. And I don't understand it. Every failed replication should teach us something new. Something about the bounds of where a method works.
It's odd. In our strive for novelty we sure do turn down a lot of novel results. In our strive to reduce redundancy we sure do create a lot of redundancy.
Sometimes the result is wrong, or it's not as big or as general as claimed. Or maybe the provided instructions are insufficient to replicate the work. But sometimes the attempt to replicate a result fails, because the person doing it does not understand the topic well enough.
Maybe they are just doing the wrong things, because their general understanding of the situation is incorrect. Maybe they fail to follow the instructions correctly, because they have subtle misunderstandings. Or maybe they are trying to replicate the result with data they consider similar, but which is actually different in an important way.
The last one is often a particularly difficult situation to resolve. If you understand the topic well enough, you may be able to figure out how the data is different and what should be changed to replicate the result. But that requires access to the data. Very often, one side has the data and another side the understanding, but neither side has both.
Then there is the question of time. Very often, the person trying to replicate the result has a deadline. If they haven't succeeded by then, they will abandon the attempt and move on. But the deadline may be so tight that the authors can't be reasonably expected to figure out the situation by then. Maybe if there is a simple answer, the authors can be expected to provide it. But if the issue looks complex, it may take months before they have sufficient time to investigate it. Or if the initial request is badly worded or shows a lack of understanding, it may not be worth dealing with. (Consider all the bad bug reports and support requests you have seen.)
I definitely think all these are important, even if in different ways. For the subtle (and even not so subtle) misunderstandings it matters who misunderstands. For the most part, I don't think we should concern ourselves with non-experts. We do need science communicators, but this is a different job (I'm quite annoyed at those on HN who critique arxiv papers for being too complex while admitting they aren't researchers themselves). We write papers to communicate to peers, not the public. If we were to write to the latter each publication would have to be prepended by several textbooks worth of material. But if it is another expert misunderstanding, then I think there's something quite valuable there. IFF the other expert is acting in good faith (i.e. they are doing more than a quick read and actually taking their time with the work) then I think it highlights ambiguity. I think the best way to approach this is distinguish by how prolific the misunderstanding is. If it is uncommon, well... we're human and no matter how smart you are you'll produce mountains of evidence to the contrary (we all do stupid shit). But if the misunderstanding is prolific then we can be certain that ambiguity exists, and it is worth resolving. I've seen exactly what you've seen as well as misunderstandings leading to discoveries. Sometimes our idiocracy can be helpful lol.
But in any case, I don't know how we figure out which category of failures it is without it being published. If no one else reads it it substantially reduces the odds of finding the problem.
FWIW, I'm highly in favor of a low bar to publishing. The goal of publishing is to communicate to our peers. I'm not sure why we get so fixated on these things like journal prestige. That's missing the point. My bar is: 1) it is not obviously wrong, 2) it is not plagiarized (obviously or not), 3) it is useful to someone. We do need some filters, but there's already natural filters beyond the journals and conferences. I mean we're all frequently reading "preprints" already, right? I think one of the biggest mistakes we make is conflate publication with correctness. We can't prove correctness anywhere, science is more about the process of elimination. It's silly to think that the review process could provide correctness. It can (imperfectly) invalidate works, but not validate them. It isn't just the public that seems to have this misunderstanding...
Things are easier when you are writing to your peers within an established academic field. But all too often, the target audience includes people in neighboring fields. Then it can easily be that most people trying to replicate the work are non-experts.
For example, most of my work is in algorithmic bioinformatics, which is a small field. Computer scientists developing similar methods may want to replicate my work, but they often lack the practical familiarity with bioinformatics. Bioinformaticians trying to be early adopters may also try to replicate the work, but they are often not familiar with the theoretical aspects. Such a variety of backgrounds can be a fertile ground for misunderstandings.
Sure. You can't write to everyone and there's tradeoffs to broadening your audience. But I'm also not sure what your point is. That people are arrogant? Such variety of backgrounds can also be fertile ground for collaboration. Something that should happen more often
As a gross simplification, there are two kinds of fields. Some are defined by the methods they use, and some by the topics they study.
The latter will use any methods that may yield results. That creates a problem. The people who are in the target audience for a paper and may try to replicate the results often fail to do so, because they lack the expertise. Because their background is too different.
I think you think that because we don't agree that I have some grave misunderstanding of some, to be frank, basic facts. I assure you, I perfectly understand what you're bringing up here and in the last comment.
But I think you still haven't understood my point about trade-offs. At least you aren't responding as if these exist.
Our disagreement isn't due to lack of understanding the conditions, it is due to a difference in acceptable limitations. After all, perfection doesn't exist.
So you can't just solve problems like this by bringing up limitations in an opposing viewpoint. I assure you, I was already well aware of every single one you've mentioned...
My original point was that replication attempts often fail, because the person trying to replicate the result is not an expert in the field, and they do not have enough time to devote to the effort. This is a common situation in fields that use results from other fields. If they don't have the time for proper replication, they probably don't have the time for publishing the attempt.
As for your point, I don't really understand what you are trying to say.
Advanced groups usually replicate their competitor's results in their own hands shortly after publication (or they just trust their competitor's competence). But they don't spend any time publishing it unless they fail to replicate and can explain why they can't replicate. From their perspective, it's a waste of time. I think this has been shown to be a naive approach (given the high rate of image fraud in molecular biology) but people who are in the top of the field have strong incentives to focus on moving the state of the art forward without expending energy on improving the field as a whole.
"strong incentives to focus on moving the state of the art forward without expending energy on improving the field as a whole"
That sort of Orwellian doublethink is exactly the problem. They need to move it forward without improving it, contribute without adding anything, challenge accepted dogma without rocking the boat, and...blech!
> challenge accepted dogma without rocking the boat
I think the funniest part is how we have all these heroes of science who faced scrutiny by their peers, but triumphed in the end. They struggled because they challenged the status quo. We celebrate their anti authoritative nature. We congratulate them for their pursuit of truth! And then get mad when it happens. We pretend this is a thing of the past, but it's as common as ever[0,1].
You must create paradigm shifts without challenging the current paradigm!
Top journals are not inherently prestigious. They are prestigious because they try to publish only the most interesting and most significant results. If they started publishing successful replication studies, they would lose prestige, and more interesting journals would eventually rise to the top. (Replication studies that fail to replicate a major result in a spectacular way are another matter.)
Are you explaining this from experience or from speculation?
I can tell you that it doesn't match my own experience. I also think it doesn't match your example. Those cases of verified image fraud are typically part of replication efforts. The reason the fraud is able to persist is due to the lack of replication, not the abundance of it.
Mostly experience (based on being a PhD scientist, a postdoc, a National Lab scientist, and engineer at several bigtech companies), partly speculation (none of the groups/labs I worked in operated at "the highest level", but I worked adjacent to many of those).
I'm pretty sure most image fraud went completely unrealized even in the case of replication failure. It looks like (pre AI) it was mostly a few folks who did it as a hobby, unrelated to their regular jobs/replication work.
In most of the labs I've worked in replication is not a common task[0]
> 'm pretty sure most image fraud went completely unrealized even in the case of replication failure
Part of my point is that being unable to publish replication efforts means we don't reduce ambiguity in the original experiments. I was taught that I should write a paper well enough that a PhD student (rather than candidate) should be able to reproduce the work. IME replication failures are often explained with "well I must be doing something wrong." A reasonable conclusion, but even if true the conclusion is that the original explanation was insufficiently clear.
> It looks like (pre AI) it was mostly a few folks who did it as a hobby
I'm sorry, didn't you say
>>> Advanced groups usually replicate their competitor's results in their own hands shortly after publication
Because your current statement seems to completely contradict your previous one.
Or are you suggesting that the groups you didn't work with (and are thus speculating) are the ones who replicate works and the ones you did work with "just trust their competitor's competence")? Because if this is what you're saying then I do not think this "mostly" matches your experience. That your experience more closely matches my own.
[0] I should take that back. I started in physics (undergrad) and went to CS for grad. Replication could often be de facto in physics, as it was a necessary step towards progress. You often couldn't improve an idea without understanding/replicating it (both theoretical and experimental). But my experience in CS, including at national labs, was that people didn't even run the code. Even when code was provided as part of reviewing artifacts I found that my fellow reviewers often didn't even look at it, let alone run it... This was common at tier 1 conferences mind you... I only knew one other person that consistently ran code.
Note that my field is biophysics (quantitative biology) while yours is physics and CS. Those are done completely differently from biology; with the exception of some truly enormous/complex/delicate experiments that require unique hardware, physics tends to be much more reproducible than biology, and CS doubly-so.
Replication of an experiment and finding image fraud are kind of done as two different things. If somebody publishes a paper with image fraud, it's still entirely possible to replicate their results(!) and if somebody publishes a paper without any image fraud, it's still entirely possible that others could fail to replicate. Also, most image errors in papers are, imho, due to sloppy handling/individual errors, rather than intentional fraud (it's one of the reasons I worked so hard on automating my papers- if I did make an error, there should be audit log demonstrating the problem, and the error should be rectified easily/quickly in the same way we fix bugs in production at big tech).
This came up a bunch when I was at LBL because of work done by Mina Bissell there on extracellular matrix. She is actively rewriting the paradigm but many people can't reproduce her results- complex molecular biology is notororiously fickle. Usually the answer is, "if you're a good researcher and can't reproduce my work, you come to my lab and reproduce it there" because the variables that affect this are usually things in the lab- the temperature, the reagents, the handling.
> physics tends to be much more reproducible than biology, and CS doubly-so.
With physics I think there is a better culture of reproduction, but that is, I believe, due more to culture. That it is acceptable to "be slow". There's a high stress on being methodical and extremely precise. The prestige is built on making your work bulletproof, and so you're really encouraged to help others reproduce your work as it strengthens it. You're also encouraged to analyze in detail and to faithfully reproduce, because finding cracks also yields prestige. I don't know if it's the money, but no one is in it for the money. Physics sure is a lot harder than anything else I've done and it pays like shit.
For CS the problem is wildly different. It should be easy to reproduce as code is trivial to copy. Ignoring the issue of not publishing code alongside results, there's also often subtle things that can make or break works. I've found many times in replication efforts that the success can rely on a single line that essentially comes form a work that was the reference to a reference of the work I'm trying to reproduce. The problem here is honestly more of laziness. In contrast to physics there's an extreme need for speed. In physics (like everyone else I knew) I often felt like I was not smart enough, and that encouraged people to dive deeper and keep improving or to give up. In CS (like everyone else I knew) I often felt like I was not fast enough, and that encouraged people to chase sponsorships from labs that provided more compute, it encouraged a "shotgun" approach (try everything), or for people to give up (aka "GPU poor").
The reason I'm saying this is because I think it is important to understand the different cultures and how replication efforts differ. In physics a replication failure was often assumed to be due to a lack of intelligence. In CS a replication effort is seen as a waste of time. Both are failures of the scientific process. Science is intended to be self-correcting. Replication is one means of this, but at its heart is the pursuit of counterfactual models. This gives us ways to validate, or invalidate, models through means other than direct replication. You can pursue the consequences of the results if you are unable to pursue the replication itself. This is almost always a good path to follow as it is the same one that leads to the extension and improvement of understanding.
There's a lot I agree and disagree with from Dr Bissell's article. Our perspectives may differ due to our different fields, but I do think it also serves as some a point of collaboration, if not on the subject of meta-science. Biology is not unique in having expensive experiments. I want to point out two famous and large physics projects: the LHC's discovery of the Higgs Boson[0] and LIGO's Observation of a Gravitational Wave[1]. The former has 9 full pages of authors (IIRC over 200) while the latter has about 3. These works are both too expensive to replicate while also demonstrating replication. Certainly we aren't going to take another 2 decades to build another CERN and replicate the experiments. But there's an easy to miss question that might also make apparent the existence of replication: who is qualified to review the paper and is not already an author of it? There's definitely some, but it really isn't that many. In these mega projects (and there are plenty more examples) the replication is done through collaboration. Independent teams examine the instruments that make the measurements. Independent teams make measurements, using the same device or different devices (ATLAS isn't the only detector at CERN), different teams independently analyze and process the information, and different teams model and simulate them. With LIGO this is also true. It would be impossible to locate those black holes without at least 2 facilities: one in Hanford (Washington) and the other in Livingston (Louisiana) (and now there's even more facilities). Astrophysics has a long history of this type of replication/collaboration as one team will announce an observation and it is a request for other observations. Observations that often were already made! In HEP (high energy particle physics) this may be less direct, but you'll notice other particle physics labs are in the author list of[0]. That's because despite the exact experiment not being replicatable in other facilities, there are still other experiments done. In the effort to find the Higgs there were many collisions performed at Fermi Lab.
I don't think this same in biophysics, but I think there are nuggets that may be fruitful. Bissell mentions at the end of her argument that she believes replication might have higher success were labs to send scientists to the original labs. I fully agree! That would follow the practice we see in these mega experiments in physics. But I also do think she's brushing off an important factor: it is far quicker and cheaper to replicate works than it is to produce them. You're a scientist, you know how the vast majority of time (and usually the vast majority of money) is "wasted" in failures (it'd be naive to call it waste). Much of this goes away with replication efforts. The greater the collaboration the greater the reduction in time and money.
And I do agree with Bissell in that we probably shouldn't replicate everything[2]. At least if we want to optimize our progress. But also I want to stress that there is no perfect system and there are many roadblocks to progress. Frankly, I'd argue that we waste far more time in things like grant writing and publication revisions. I don't know a single scientist who hasn't had a work rejected due to reviewers either not giving the work enough care or simply because they were unqualified (often working in a different niche so don't understand the minutia of the problem). As for the grant writings, I think they're a necessary evil but I'm also a firm believer of what Mervin Kelly (former director of Bell Labs) said when asked how you manage a bunch of geniuses: "you don't"[3]. You're a scientist, an expert in your domain. You already know what directions to look in. You've only gotten this far because you've been honing that skill. We don't have infinite money, so of course we have to have some bar, but we can already sniff out promising directions and we're much better at sniffing out fraud. Science has been designed to be self-correcting.
[More of a side note]
> Usually the answer is, "if you're a good researcher and can't reproduce my work, you come to my lab and reproduce it there" because the variables that affect this are usually things in the lab- the temperature, the reagents, the handling.
And we should not undermine the importance of these variables. Failures based on them are still informative. They still inform us about the underlying causal structure that leads to success. If these variables were not specified in the paper, then a replication failure shows the mistake of the writing. Alternatively a failure can bound these variables, by making them more explicit. I'm no expert in biophysics, but I'm fairly certain that understanding the bounds of the solution space is important for understanding how the processes actually work.
[2] I also would be very cautious about paid replication efforts. I am strongly against it as well as paywalls on publishing (both in creation of publication as well as the access of).
> Do you want issues of Nature and cell to be replication studies? As a reader even from within the field, im not interested in browsing through negative studies.
Actually, yes, I do. The marginal cost for publishing a study online at this point is essentially nil.
I think archives with pretty low standards for notability are a good idea. At some point though you have to pick what actually counts as interesting enough to go in the curated list that is actually suggested reading, where the prestige is attached. If there's no curation by Nature then it falls to bloggers or another journal to sift through the fire-hose and make best-of lists. Most of the value is in the curation, not the publishing. Without exclusivity there's very little signal.
> The marginal cost for publishing a study online at this point is essentially nil.
The marginal cost for doing a study remains the same, which is quite a bit. Society doesn't have unlimited scientific talent or hours. Every year someone spends replicating is a year lost to creating something new and valuable.
I know you got a ton of responses already but not caring about replicability just invalidates science as a method. If we care only about first to publish we end up in the current situation where we don't even know that we know is actually even remotely correct.
All because journals prefer novelty over confirmation. It's like a castle of cards, looks cool but not stable or long-term at all.
If you're thoroughly debunking a previous Nature paper they just might publish that. But the expectation is that you'll succeed. Publishing that sort of mundane article would reduce the prestige of getting something into the journal. Publishing in a high impact journal is only seen as an achievement in the first place because of what it implies about the content of your paper.
Realistically, everyone will say “yes” to the “do you want” question because if you’re not a reader or a subscriber you benefit from the readers reading replication studies.
I believe people will enthusiastically say yes but that they do not routinely read that journal.
I didn't understand us to only be talking about failed replication studies of previous Nature papers which would hopefully be few and far between and thus noteworthy indeed. Rather replication studies in general which on average are arguably less interesting to the reader than even the content of the typical archival journal.
They certainly will be few and far between when the system is structured to repress them. But there's reason to believe they wouldn't be as rare as you seem to think:
Are you seriously attempting to imply that Nature retractions aren't few and far between?
What's even your point here? Hopefully we are at least in agreement that Nature is seen as prestigious and worth looking through precisely because of the sort of content that they publish. Diluting that would dilute their very nature. (Bad pun very much intended sorry I just couldn't resist.)
"Are you seriously attempting to imply that Nature retractions aren't few and far between?"
No. I'm explicitly stating that they are few and far between, but perhaps (not certainly, but conceivably) they shouldn't be.
"What's even your point here?"
My point is that focusing on positive findings and neglecting negative findings perverts the mechanism that makes science work. Science isn't about proving things correct, it's about rooting out errors.
I'm not sure I agree. The system certainly isn't optimal but results aren't just dumped into a vacuum. Something is only useful if people can build on it. Even if negative results don't get published, even if it isn't optimal, by virtue of future positive results building on past things that did reproduce you get forward progress.
Regardless, I don't think that's at odds with my original assertion that becoming a venue for publishing negative results would undermine the "point" of Nature.
The missing link isn't a venue in which to publish. It's funding to do the work in the first place. Also funding to spend the time writing it up when you find that you've inadvertently been tricked into doing the work while trying to get something that builds on it to work.
"Also funding to spend the time writing it up when you find that you've inadvertently been tricked into doing the work while trying to get something that builds on it to work."
Oh there have been times would have loved to be able to apply for one of those!
"Original research" isn't worth much unless replicated, which is the entire problem being discussed in this thread. Replicating studies are great though because they tell you if the original research actually stands and is valid.
> Replicating work is far more difficult than a lot of original work.
Only if the original work was BS. And what, just because it's harder, we shouldn't do it?
I must be missing something, surely the argument isn't "other systems also disincentivize solving the problem, therefore we shouldn't work to fix this one"
Even if that negative study could save you one, two, three+ years of work for the same outcome (which you then also can't really do anything with)? Shouldn't there BE funding for replication studies? Shouldn't that count towards tenure? Part of the problem is that publications play such a heavy role in getting tenure in the first place.
I'm sure you can more narrowly tune your email alerts FFS.
> Replicating work is far more difficult than a lot of original work.
I don’t regularly read scientific studies but I’ve read a few of them.
How is it possible that a serious study is harder to replicate than it is to do originally. Are papers no longer including their process? Are we at the point where they are just saying “trust me bro” for how they achieved their results?
> Do you want issues of Nature and cell to be replication studies?
Not issues of Nature but I’ve long thought that universities or the government should fund a department of “I don’t believe you” entirely focused on reproducing scientific results and seeing if they are real
> How is it possible that a serious study is harder to replicate than it is to do originally.
They aren't. GP was on point until that last sentence. Just pretend that wasn't there. It's pretty much always much easier to do something when all the key details have been figured out for you in advance.
There is some difficulty if something doesn't work to distinguish user error from ambiguity of original publication from outright fraud. That can be daunting. But the vast majority of the time it isn't fraud and simply emailing the original author will get you on track. Most authors are overjoyed to learn about someone using their work. If you want to be cynical about it, how else would you get your citation count up?
I have worked in this particular sausage factory. Multiple funded random replications are the only thing that will save science from this crisis. The scientific method works. We need to actually do it.
Replications don't have to be in the journals either. As long as money flows, someone will do them, and that is what matters. The randomization will help prevent coordination between authors and replicators.
In a better world, negative studies and replications would count towards tenure, but that is unlikely to occur. At least half of the problem is the pressure to continuously publish positive results.
Regardless of what gets taught in school about science being objective and without ego, or having a culture of adversarial checks on each other etc., the reality is that scientists are humans and have egos and have petty feuds.
Publishing a failed replication of the work of a colleague will not earn you many brownie points. I'm stating this as an observation of what is the case, not as something that I think should be the case. If you attack other researchers like this and damage their reputation - even if for valid scientific reason - you'll have a hard time when those colleagues sit on committees deciding about your next grant etc.
Of course if you discover something truly monumental that will override this. But simply sniping down the mediocre research published by other run-of-the-mill researchers will get you more trouble than good. Yes it's directly in contradiction to the textbook-ideal of what science should be, as described to high school students, but there are many things in life this way.
Of course it can be laudable to go on such a crusade despite all this, and to relentlessly pursue scientific truth, etc. but that just won't scale.
You are absolutely correct. Even distributing the replication around the world will only help so much. It's a small world out there and only smaller in the specializations.
That's why replication has to be required and standard. It will hurt to tear off the bandaid, but once the culture shifts, people will hesitate to publish mediocre research in the first place. Without mediocre research flooding the zone, real numbers will dominate and inflated expectations will wither.
> That's why replication has to be required and standard.
"has to be required"... This is a passive construct. Who will do the requiring and what precisely will motivate them to such a change and what will get them the buy-in from the other players in this whole ecosystem, especially the ones who provide the money? What if it turns out that those people who do the funding actually in the deepest of their deepest are fine with "groundbreaking" research results that simply sound like being "groundbreaking" research results to such an extent that their prestige and social status rises enough and are seen as someone who funds such research, instead of truly caring about the actual contents of said research? There is much more demand (backed with money) for (plausibly-claimable) innovation and breakthroughs than supply of real novel thought. It's a bit like the anecdote that all the True Cross relics across Catholic churches weigh more than the cross Jesus carried (not really true as a fact though). As long as there is such strong demand, the system will adapt to allow for the supply finding its way.
Is there a viable career path for researchers who choose to focus on replication instead of novel discoveries? I assume replications are perceived as less prestigious, but it's also important work.
The closest thing we have is, in security / privacy / cryptography, you can write "attack" papers.
It's not perfect. You don't get any credit unless you can demonstrate a substantial break of the prior work. But it's better than in a lot of other fields.
Mainstream journals are complicit, but are not the biggest problem.
The biggest problem by far is modern society: Tenure, getting paid a livable wage as a researcher, not getting stack-ranked and eliminated from your organization all overindex on positive research results that are marketable. This "loss function" encourages scientific fraud of sorts.
When, in those mythical non-"modern" times, was it easy to get tenure or a livable wage as a researcher? How open were the doors to this and what proportion of society got a realistic chance to pursue such a career? More people getting a chance means more fierce competition.
This isn’t about honest researchers resorting to fraud to publish their null results
because they were blocked by big bad Nature. It’s about journals and authors churning out pure junk papers whose only goal is to game metrics like citation count.
Right, it seems that many of the weaknesses in the system exist because they serve the interests of journal publishers or of normal, legitimate-ish researchers, but in the process open the door to full-time system-hackers and pure fraudsters.
Any system that grows too fast has these kinds of problems. When it's a small intimate circle where everyone knows everyone, reputation alone can keep people in check. Once it's larger you need to invent rules and bureaucracies and structures and you will have loopholes that bad actors can more easily exploit, hiding in the crowd, than in the small version. It's the same with the Internet or computing. Security was much less of a topic when it was mostly honest academic nerds using the Internet, and the protocol designs often didn't even assume adversarial participants. Science also still runs on this assumed honesty system that worked well when it was small.
Yes, as long as (for example) you have a huge number of people desperate to claim one or more of a very small number of slots for papers in a few prestige journals then inevitably there must be either a cattle-call selection process, or blatant preference for existing insiders, or both. But things like the fancy journals' reluctance to acknowledge or care about errors in the papers they do publish really aren't at all a necessary consequence of a high submission ratio or of a rapid growth in submissions. In fact it's not even clear that they are a consequence of those pressures at all, let alone that they do anything to mitigate them. They're just things which serve the interests of insiders while in fact making the consequences of the Darwinian pressures worse.
> Most will refuse to publish replications, negative studies, or anything they deem unimportant, even if the study was conducted correctly.
I think this was really caused by the rise of bureaucracy in academia. Bureaucrats favorite thing is a measurement, especially when they don't understand its meaning. There's always been a drive for novelty in academia, it's just at the very core of the game. But we placed far too much focus on this, despite the foundation of science being replication. We made a trade, foundation for (the illusion of) progress. It's like trying to build a skyscraper higher and higher without concern for the ground it stands on. Doesn't take a genius to tell you that building is going to come crashing down. But proponents say "it hasn't yet! If it was going to fall it would have already" while critics are actually saying "we can't tell you when it'll fall, but there's some concerning cracks and we're worried it'll collapse and we won't even be able to tell we're in a pile of rubble."
I don't know what the solution is, but I do know that our fear of people wasting money and creating fraudulent studies has only resulted in wasting money and fraudulent studies. We've removed the verification system while creating strong incentives to cheat (punish or perish, right?).
I think one thing we do need to recognize is that in the grand scheme of things, academia isn't very expensive. A small percentage of a large number is still a large number. Even if half of academics were frauds it would be a small percentage of waste, and pale in comparison to more common waste, fraud, and abuse of government funds.
From what I can tell, the US spent $60bn for University R&D in 2023[0] (less than 1% of US Federal expenditures). But in that same time there was $400bn in waste and fraud through Covid relief funds [1]. With $280bn being straight up fraud. That alone is more than 4x of all academic research funding!!!
I'm unconvinced most in academia are motivated by money or prestige, as it's a terrible way to achieve those things. But I am convinced people are likely to commit fraud when their livelihoods are at stake or when they can believe that a small lie now will allow them to continue doing their work. So as I see it, the publish or perish paradigm only promotes the former. The lack of replication only allows, and even normalizes, the latter. The stress for novelty only makes academics try to write more like business people, trying to sell their product in some perverse rat race.
So I think we have to be a bit honest here. Even if we were to naively make this space essentially unregulated it couldn't be the pinnacle of waste, fraud, and abuse that many claim it is. But I doubt even letting scientists be entirely free from publication requirements that you'd find much waste, fraud, and abuse. Science has a naturally regulating structure. It was literally created to be that way! We got to where we are in through this self regulating system because scientists love to argue about who is right and the process of science is meant to do exactly that. Was there waste and fraud in the past? Yes. I don't think it's entirely avoidable, it'll never be $0 of waste money. But the system was undoubtably successful. And those that took advantage of the system were better at fooling the public than they were their fellow scientists. Which is something I think we've still failed to catch onto
You either have something documented and quantified and measured and objective criteria tickboxes and deal with this style of failure mode, or you rely on subjective judgment and assessment and accept the failure mode of bias, nepotism, old boy's clubs etc. Of course the ideal case is to rely on the unbureaucratic informal wise and impartial judgment of some hypothetical perfect humans you can fully trust and rely on, and they always decide fully on merits etc. without having to follow any rigid criteria and checkboxes and numbers on hiring and promotion etc. But people are not perfect and society largely decided to go the bureaucratic way to ensure equal opportunities and to reduce bias through this kind of transparency.
> You either have something documented and quantified and measured and objective criteria tickboxes and deal with this style of failure mode, or you rely on subjective judgment and assessment and accept the failure mode of bias, nepotism, old boy's clubs etc
My argument is that our current pursuit of the former only reinforces the existence of the latter.
You have a fundamental flaw in your argument, one that illustrates a common, yet fundamental, misunderstanding of science. There is no "objective" thing to measure, there are only proxies. I actually recently stumbled on a short by Adam Savage that I think captures this[0], although I think he's a bit wrong too. Regardless of precision we are always using a proxy. A tape measure does not define a meter, it only serves as a reference to compare with. A reference where not only the human makes error when reading, but that the reference itself has error[1]. So there are no direct measurements, there are only measurements by proxy.
You may have heard someone say "science doesn't prove things, it disproves them", and that's in part a consequence to this. Our measurements are meaningless without an understanding of their uncertainty (both quantifiable and unquantifiable!) as well as the assumptions they are made under.
I'm not trying to be pedantic here, I think this precision in understanding matters to the conversation. My argument is that by discounting those errors that they accumulate. We've had a pretty good run. This current system has only really started to be practiced in the 60s and 70's. So 50 years is a lot of time for error to accumulate. 50 years is a lot of time for small, seemingly insignificant, and easy to dismiss errors to accumulate into large, intangible, and complex problems.
There's something that I guess is more subtle in my argument: science is self-correcting. I don't mean "science" as the category of pursuits that seek truths about the world around us, but I mean "science" as a systematic approach to obtaining knowledge. A key reason this self-correction happens is due to replication. But in reality that is a consequence of how we pin down truth itself. We seek causal structures. More specifically, we seek counterfactual models. Assuming honest practitioners, failures of reproduction happen for primarily for one of two reasons: 1) ambiguity of communication between the original experimenters and those replicating or 2) a variation in conditions. 2) is actually quite common and tells us something new about that causal structure. In practice it is extremely difficult, if not impossible, to exactly replicate the conditions of the original experiment, so even with successful replication we gain information about the robustness of the results.
But why am I talking about all this? Because without the explicit acknowledgement of these limitations we seem to easily forget them. We are often treating substantially more subjective measures (such as impact or novelty) as far more objective than we would treat even physical measurements. It should be absolutely no surprise that things like impact are at best extremely difficult to measure. Even with a time machine we may not accurately measure the impact of a work for decades, or more. Ironically, a major reason for a work's impact to be found only after decades (or centuries) is the belief that at its time it had no impact, and was a dead end. You'd be amazed at how common this actually is. It's where jokes similar to how everything is named after the second person to discover something, the first being Euler[2]. But science is self-correcting. Even if a discovery of Euler's was lost, it is only a matter of time before someone (independently) rediscovers it.
I'm talking about this because there is no perfect system. Because a measurement without the acknowledge of its uncertainty is far less accurate than a measurement with. I'm talking about this because we will always have errors and the existence of them is not a reason to dismiss things. Instead we have to compare and contrast both the benefits and limits of competing ideas. We are only doing ourselves a disservice by pretending the limits don't exist. And if we mindlessly pursue objective measurements we'll only end up finding we've metric hacked our way into reading tea leaves. As we advance in any subject the minutia always ends up being the critical element (see [0]) and so the problem is it doesn't matter if we're 90% "objective" and 10% reading the tea leaves. Not when the decisions are made differentiating the 10%. In reality we're not even good at measuring that 90% when it comes to determining how productive academics are[3-5]
[5] See the two links in this comment as further evidence. They are about relatively recent Nobel works that faced frequent rejections https://news.ycombinator.com/item?id=47340733
Someone has to pay for all this. That someone is most often not a scientist themselves. They don't have a that vague intuitive research taste that scientists have. Beyond fairly trivial levels of technical correctness, the value of research lies in its narrative implications, its interestingness, its surprise factor etc. These are not objective and are often more about aesthetics and taste than popularly understood. Why does the research matter? To whom does it matter? Are those people important? Do they control resources?
There's a cost in either direction. You can't ignore the the costs of reading the tea leaves while acknowledging the costs of unnecessary work. Both have costs.
>> Instead we have to compare and contrast both the benefits and limits of competing ideas. We are only doing ourselves a disservice by pretending the limits don't exist.
> But in that same time there was $400bn in waste and fraud through Covid relief funds [1].
The cost of academic fraud should also include the indirect costs of bad decision making.
The Covid relief funds were only needed because politicians implemented extremely aggressive policies based on unproven epidemiological models built on fraudulent practices. I investigated all this extensively at the time and it was really sad/shocking how non-existent intellectual standards are in the field of epidemiology. The models were trash RNGs that couldn't have been validated even if they'd tried, which they never had because the field doesn't consider validation to be necessary to get a paper published. So the models made wildly wrong predictions based on untested, buggy, non-replicable models, which then led to lockdowns, which led to economic catastrophe, which led to the relief programme. All of the fraud in that programme - really the entire cost of it - should be laid at the feet of academic fraud.
This is Goodhart's law at scale. Number of released papers/number of citations is a target. Correctness of those papers/citations is much more difficult so is not being used as a measure.
With that said, due to the apparent sizes of the fraud networks I'm not sure this will be easy to address. Having some kind of kill flag for individuals found to have committed fraud will be needed, but with nation state backing and the size of the groups this may quickly turn into a tit for tat where fraud accusations may not end up being an accurate signal.
Also, Brandolini's law. And Adam Smith's law of supply and demand. When the ability to produce overwhelms the ability to review or refute, it cheapens the product.
> Number of released papers/number of citations is a target
There was this guy, well connected in the science world, that managed to publish a poor study quite high (PNAS level). It was not fraud, just bad science. There were dozens of papers and letters refuting his claims, highlighting mistakes, and so... Guess what? Attending to metrics (citations, don't matter if they are citing you to say you were wrong and should retract the paper!), the original paper was even more stellar on the eyes of grants and the journal itself.
There’s an accurate way to confirm fraud: look for inconsistencies and replicate experiments.
If the fraudsters “fail to replicate” legitimate experiments, ask them for
details/proof, and replicate the experiment yourself while providing more details/proof. Either they’re running a different experiment, their details have inconsistencies, or they have unreasonable omissions.
Of course this is slightly messy too. Fraudsters are probably always incorrect, of course they could have stolen the data. But being incorrect doesn't mean your intentionally committing fraud.
That would be great if journals bothered publishing replication studies. But since they don't, researchers can't get adequate funding to perform them, and since they can't perform them, they don't exist.
We can't look for failed replication experiments if none exist.
That only confirms a very small subset of fraud. There are many ways to do scientific fraud that will yield internally consistent papers that pass replication as practiced today.
An example is papers which claims of the form, "We proved X by doing Y" where Y is a methodology that isn't derived from and can't prove X. This sort of paper will replicate every time because if you re-derive a correct methodology the original authors say you didn't really replicate their study and your work should be ignored, but if you use their broken methodology you'll just give an intellectually fraudulent paper the stamp of replication approval.
This kind of problem is actually much more widespread than work that looks scientific but in which the data is faked.
What do you think it is about machine learning that makes it hard to replicate? I'm an outsider to academic research, but it seems like computer based science would be uniquely easy - publish the code, publish the data, and let other people run it. Unless it's a matter of scale, or access to specific hardware.
A lot of things are easy if you ignore the incentive structure. E.g. a lot of papers will no longer be published if the data must be published. You’d lose all published research from ML labs. Many people like you would say “that’s perfectly okay; we don’t need them” but others prefer to be able to see papers like Language Models Are Few-Shot Learners https://arxiv.org/abs/2005.14165
So the answer is that we still want to see a lot of the papers we currently see because knowing the technique helps a lot. So it’s fine to lose replicability here for us. I’d rather have that paper than replicability through dataset openness.
But the lab must publish at least the general category of data, and if that doesn't replicate, then the model only works on a more specific category than they claim (e.g. only their dataset).
Even with the exact same dataset and architecture, ML results aren't perfectly replicable due to random weight initialisation, training data order, and non-deterministic GPU operations. I've trained identical networks on identical data and gotten different final weights and performance metrics.
This doesn't mean the model only works on that specific dataset - it means ML training is inherently stochastic. The question isn't 'can you get identical results' but 'can you get comparable performance on similar data distributions.
Then researchers should re-train their models a couple times, and if they can't get consistent results, figure out why. This doesn't even mean they must throw out the work: a paper "here's why our replications failed" followed by "here's how to eliminate the failure" or "here's why our study is wrong" is useful for future experiments and deserves publication.
As per my previous comment - we are discussing stochastic systems.
By definition, they involve variance that cannot be explained or eliminated through simple repetition. Demanding a 'deterministic' explanation for stochastic noise is a category error; it's like asking a meteorologist to explain why a specific raindrop fell an inch to the left during a storm replication.
Lack of will.
That was one of the main results from the survey from Whitaker in 2020.
Making your code reusable and easy to understand is significant work that had no direct benefits for a researcher's career. Particularly because research code grows wildly as researchers keep trying thungs.
Working on the next paper is seem as the better choice.
Moreover if your code is easy for others to run then you're likely to be hit with people wanting support, or even open yourself to the risk of someone finding errors in your code (the survey's result, not my own beliefs).
There are other issues, of course. Just running the code doesn't mean something is replicable. Science is replicated when studies are repeated independently by many teams.
There are many other failure modes SOTA-hacking, benchmarking, and lack of rigorous analysis of results, for example. And that's ignoring data leakage or other more silly mistakes (that still happen in published work! In work published in very good venues even)
Authors don't do much of anything to disabuse readers that they didn't simply get really look with their pseudorandom number generators during initialization, shuffling, etc. As long as it beats SOTA who cares if it is actually a meaningful improvement? Of course doing multiple runs with a decent bootstrap to get some estimation of the average behavior os often really expensive and really slow, and deadlines are always so tight. There is also the matter that the field converged on a experimentation methodology that isn't actually correct. Once you start reusing test sets your experiments stop being approximations of a random sampling process and you quickly find yourself outside of the grantees provided by statistical theory (this is a similar sort of mistake as the one scientists in other fields do when interpreting p-values). There be dragons out there and statistical demons might come to eat your heart or your network could converge to an implementation of nethack.
Scale also plays into that, of course, and use of private data as the other comment mentioned.
Ultimately Machine Learning research is just too competitive and moves too fast. There are tens of thousands (hundreds maybe?) of people all working on closely related problems, all rushing to publish their results before someone else published something that overlaps too much with their own work. Nobody is going to be as careful as they should, because they can't afford to. It's more profitable to carefully find the minimal publishable amount of work and do that, splitting a result into several small papers you can pump every few months. The first thing that tends to get sacrificed during that process is reliability.
>>95% of the time, the fraudsters get off scot-free. Look at Dan Ariely: Caught red-handed faking data in Excel using the stupidest approach imaginable, and outed as a sex pest in the Epstein files. Duke is still giving him their full backing.
It’s easy to find fraud, but what’s the point if our institutions have rotten all the way through and don’t care, even when there’s a smoking gun?
> Number of released papers/number of citations is a target
Only in stupid university leaderships is that truly what gets you hired or promoted. It's simply not true. Junior researchers in fact are believing it stronger than the facts actually support. Yes, you have to have a solid amount of publications, but doing a ridiculous amount of low-impact salami-sliced stuff or getting your name on a ton of papers where you did no real work is not going to win you a job. People who evaluate applications also live in this world and know that these metrics are being gamed. It's a cat and mouse game but the cats are also paying attention. You can only play this against really dumb government bureaucracies that mechanically give points for publications and have hard numerical criteria etc. Good institutions don't do that.
Good evaluators actually read the papers themselves. Of course you can't read the papers of every single applicant if there are many. But once the applicant gets into the a somewhat filtered down list, reading the paper(s) or having an interview about it, or having them give a talk is much more informative than the number of the papers. Still not perfect, because some people can't communicate well, but communicating is part of the job, so maybe that's super bad but somewhat bad.
Evaluators will use also other evidence such as recommendation letters (informally being aware of the reputation of the recommender), previous fellowships or grants obtained, etc.
None of these are foolproof in themselves. But someone who has super few publications relative to their career stage will need some other piece of evidence in favor.
In machine learning and AI, peer reviews are known to be quite random. If you have a good Arxiv-only paper that makes sense and you can give a good talk on it and answer questions, that will get you further than having a rubberstamp on some paper that's "meh, so what".
There are some players in this game (which includes funding agencies, journals, university administration, hiring committees, conference organizers, students, etc) that are more ossified and slow-moving than others.
And it's also true that double blind peer review and the rubberstamp of a top-tier conference was mostly beneficial to small, not well connected research groups, as it puts the paper on an equal footing with the big labs. The more this system erodes, to more we fall back to reputation and branding of big labs and famous researchers. Again, because there is no infinite time and infinite wisdom available to pick from applicants and there never will be. There are only tradeoffs.
I ran into an interesting incident of this recently. I got a Google Scholar alert about a paper with some experiments related to a paper I had published a while ago, by one "N. Tvlg". I read the paper with interest but I started noticing that although the arguments sounded good, they didn't really make sense, and also the descriptions of the results didn't really match the figures. Eventually I came across a cluster of citations to completely unrelated papers---my field is computational linguistics and these were citations to, like, studies of battery technologies for electric cars. I looked up "N Tvlg" on Google Scholar and they had "published" several articles very recently in totally divergent fields, and upon inspection, all of them had citations back to this materials science research buried deeply somewhere. Clearly these were LLM generated papers trying to build up citation count and h-rank for someone's career.
The purpose of scientific publication used to be to deliver useful scientific results to one's peers. This meant that everyone ran their own personal filter of which peers were working on interesting things, and which collections (journals) were reproducing the most interesting ones. This system still works relatively well for most conscientious researchers. The idea that we should also use publication metrics to rank researchers was never part of this system, and it obviously leads to all sorts of spam (that most scientists just work around) but that seems to really upset non-scientists.
and select "Mathematics and Computer Science", you'll find the top-ranked university is the University of Electronic Science and Technology of China.
My Chinese colleagues have heard of it, but never considered it a top-ranked school, and a quick inspection of their CS faculty pages shows a distinct lack of PhDs from top-ranked Chinese or US schools. It's possible their math faculty is amazing, but I think it's more likely that something underhanded is going on...
It's strange to me that in places full of smart people, it seems to be well understood that this happens and there are lots of anecdotes relating to it; yet the same people will be confused that their political adversaries don't trust "the science" on one issue or another.
That’s the beautiful thing about science: You do not have to (and should not) trust any individual. And even if you don’t trust “the consensus” of “the scientific community”, you can empirically verify yourself.
Once you move from abstract to practical - like say having legislators or regulators make rules based on The Science, or relying personally on more facts than you have time to independently verify - yes you do need to have trustworthy people.
This is a common misconception in discussions about scientific fraud. You don't have to be able to do a thing correctly to detect when it's being done incorrectly.
You shouldn't trust any claims by scientists about global trends in mean temperatures. We can say this with confidence, without being able to compute a better timeseries, by just looking to see if the basics of the scientific method are being followed by those who do it. If we do that check we find that they don't follow the scientific method. Specifically, they edit past observations to bring them into line with theory instead of deriving theory from data believed to be robust.
No, but the literature is open for you to read. Thus you can judge the stated reasoning for yourself. You can also assess how many independent groups are making the same (or closely related) claim.
If only one person claims X then it might be fraud. If large numbers of seemingly unrelated people all claim X then you're forced to decide between X and a global conspiracy to misrepresent X.
To your example. Importantly, even if you deemed one of the global mean temperature datasets to be untrustworthy there are other related (but different) datasets. There are also other pieces of evidence related to the downstream claims that don't look directly at temperature.
I think it's difficult to relay to the public that a lot of this noise in "scientific publications" is not the same category as real research by reputable institutions. Yes, in certain cases the line can be blurry, fraudsters are sometimes caught in big-name institutions, maybe more in some fields than others, but serious researchers of the field know very well which publication venues and research groups are the real deal and what is bullshit. Overwhelmingly, these fraud papers and nonsense LLM-generated fake stuff are not published in serious journals or conferences.
It's a bit like how can we trust online shopping if I get all these emails trying to sell me aphrodisiac pills?
the "political adversaries" (let's not be coy, you mean conservatives) who "don't trust 'the science'" are less equipped to evaluate said "the science" than the scient_ists_ they don't trust. usually said people were one bad day away from rambling about global jewry and such. due to whatever social illnesses or sociopolitical causes, we've normalized this position, and now measles is back from the dead. but let's pretend that climate change isn't real and the earth's flat and vaccines give you autism, because universities too woke or whatever the fuck
(it's very funny to pretend tech is still, or really much ever was, one big libfest. it's funnier to say this here of all places)
It is useful to distinguish between "effective" scientific fraud, where some set of fraudulent papers are published that drive a discipline in an unproductive direction, and "administrative" scientific fraud, where individuals use pseudo-scientific measures (H-index, rankings, etc) to make allocation decisions (grants, tenure, etc). This article suggests that administrative scientific fraud has become more accessible, but it is very unclear whether this is having a major impact on science as it is practiced.
Non-scientists often seem to think that if a paper is published, it is likely to be true. Most practicing scientists are much more skeptical. When I read a that paper sounds interesting in a high impact journal, I am constantly trying to figure out whether I should believe it. If it goes against a vast amount of science (e.g. bacteria that use arsenic rather than phosphorus in their DNA), I don't believe it (and can think of lots of ways to show that it is wrong). In lower impact journals, papers make claims that are not very surprising, so if they are fraudulent in some way, I don't care.
Science has to be reproducible, but more importantly, it must be possible to build on a set of results to extend them. Some results are hard to reproduce because the methods are technically challenging. But if results cannot be extended, they have little effect. Science really is self-correcting, and correction happens faster for results that matter. Not all fraud has the same impact. Most fraud is unfortunate, and should be reduced, but has a short lived impact.
The distinction between effective and administrative fraud is useful and I think underappreciated. A lot of the conversation in these threads conflates the two, which makes it hard to reason about what actually needs fixing.
I want to push back a little on "science is self-correcting" though. It's true in the limit, but correction has a latency, and that latency has real costs. In fields like nutrition, psychology, or pharmacology, a fraudulent or deeply flawed result can shape clinical guidelines, public policy, and drug development pipelines for a decade or more before the correction lands. The people harmed during that window don't get made whole by the eventual retraction.
The comparison I keep coming back to is fault tolerance in distributed systems. You can build a system that's "eventually consistent" and still have it be practically broken if convergence takes too long or if bad state propagates faster than corrections do. The fraud networks described in TFA are basically an adversarial workload against a system (peer review) that was designed for a much lower rate of bad input. Saying the system self-corrects is accurate, but it's not the same as saying the system is healthy or that the current correction rate is adequate.
I think the practical question isn't whether science corrects itself in theory but whether the feedback loops are fast enough relative to the rate of fraud production, and right now the answer seems pretty clearly no.
Re self-correcting science. In the area I am most familiar with (basic life sciences), correction happens pretty quickly. But I don’t worry about public policy much.
But I’m comfortable arguing that where science intersects with policy, fraud plays a very minor role. I suspect that most policy “mistakes” (policies that were adopted and then reversed) are more about the need for a policy in the absence of data (covid and masks), or subtle tradeoffs (covid and masks), or a policy choice that seems slightly better than an alternative (mammography) but also has poorly understood harms. Policy involves politics, and science unfortunately plays less of a role than one might like (and fraudulent science an even smaller role). This is not my field, but I cannot think of policies that were reversed because of discoveries of fraud (perhaps thalidomide and other drug approvals).
My wife completed her PhD two years ago and she put a LOT of work into it. Many sleepless nights, and it almost destroyed our marriage. It took her about 6 years of non-stop madness and she didn’t even work during that time. She said that many of her colleagues engaged in fraudulent data generation and sometimes just complete forgery of anything and everything. It was
obvious some people were barely capable of putting together coherent sentences in posts, but somehow they generated a perfect dissertation in the end. It was common knowledge that candidates often hired writers and even experts like statisticians to do most of the heavy lifting. I don’t know if this is the norm now, but I simultaneously have more respect and less respect for those doctoral degrees, knowing that some poured their heart and soul into it, while others essentially cheated their way through. OTOH, I also understand that there may be a lot of grey area.
I found the article and your third-hand anecdotes troubling. The good news is that it does not match any of the years of experience in my field. Fraud is just not that rampant. At PhD-granting institutions, the level of fraud you describe here is very seriously punished. It's career-ending. The violations that you are serious enough that any institution would expel said students (or harshly punish faculty--probably firing them). She did no one any favors by not reporting them.
Unfortunately I don't think a dialogue around vague anecdotes is going to be particularly enlightening. What matters is culture, but also process--mechanisms and checks--plus consequences. Consequences don't happen if everyone is hush-hush about it and no one wants to be a "rat".
That is where being good at politics come into play. And if you are good at it, instead of being career-ending, fraud will put you in the highest of the positions!
No one wants a "plant" who cannot navigate scrutiny!
> The good news is that it does not match any of the years of experience in my field.
I worked for exactly one academic, and he indulged in impossible-to-detect research fraud. So in my own limited experience research fraud was 100%.
It was a biology lab, and this was an extremely hard working man. 18 hours per day in the lab was the norm. But the data wasn't coming out the way he wanted, and his career was at stake, so he put his thumb on the scale in various ways to get the data he needed. E.g. he didn't like one neural recording, so he repeated it until he got what he wanted and ignored the others. You would have to be right in the middle of the experiment to notice anything, and he just waved me off when I did.
This same professor was the loudest voice in the department when it came to critiquing experimental designs and championing rigor. I knew what he did was wrong, because he taught me that. And he really appeared to mean it, but when push came to shove, he fiddled, and was probably even lying to himself.
So I came away feeling that academic fraud is probably rampant, because the incentives all align that way. Anyone with the extraordinary integrity to resist was generally self-curated out of the job.
I had a somewhat similar experience- was a postdoc for a pre-tenure professor at berkeley. after writing up a paper based on her methods, with poor results, I handed the draft to her. She rewrote it- basically adding carefully worded/presented results that made it look as good as possible. And then submitted it (to a niche conference where the editor was a buddy of hers). When I read her submission I asked her to remove my name from it and she immediately withdrew the submission. I left her lab shortly after because I am not going to tarnish my publication record with iffy papers like that.
Over time I learned that most papers in my field (computational biology) are embellished to some extent or another (or cherry-picked/curated/structured for success) and often irreproducible- some key step is left out, or no code is provided that replicates the results, etc. I can see this from two perspectives:
1) science should be trivially reproducible; it should not require the smartest/most capable people in the field to read the paper and reproduce the results. This places a burden on the people who are at the state of the art of the field to make it easy for other folks, which slows them down (but presumably makes overall progress go faster).
2) science should be done by geniuses; the leaders in the field don't need to replicate their competitors paper. it's sufficient to read the paper, apply priors, and move on (possibly learning whatever novel method/technique the paper shows so they can apply it in their own hands). It allows the field innovators to move quickly and discover new things, but is prone to all sorts of reliability/reproducibility problems, and ideally science should be egalitarian, not credentials-based.
I have repeated it many times on this site but here’s the reality of human experience: if the rate of fraudulent labs is even as high as 10% you should expect that any viewpoint that it’s widespread would be drowned out by views that it’s not real.
Also, the phenomenon you observed where people are champions till the rubber meets the road is more common than one thinks.
> if the rate of fraudulent labs is even as high as 10% you should expect that any viewpoint that it’s widespread would be drowned out by views that it’s not real.
If "it" is fraud here I would expect the viewpoint that it's widespread to be less and less drowned out as it approached 10% since everyone would know that it's real. I think I'm misunderstanding the sentence.
No, the guys at fraudulent labs and the guys at honest labs will both claim no fraud. The only ones who will claim fraud are those who cross over. So you’ll get a vast majority telling you it’s not happening and a tiny minority (even when as high as 10% are fraud) telling you the fact. All rare things have this effect. There will be so many people telling you it’s not real “as someone in the field”. They will be adamant about it. You need someone who has seen both.
To be clear, not “as it approaches 10%”. I mean “even as high as 10%”.
Not I didn't say that it isn't real, I just said it's not that rampant, and I was referring to the egregious examples in the article and the anecdote. But it's a sliding scale and being selective in presenting research results is a gray area that requires us to think about reproducibility and make sure we have good processes for discouraging, detecting, and correcting those kinds of dishonesty, too.
In my field (programming languages and compilers), conferences have started to adopt artifact submissions which include source and build instructions that are then independently replicated by volunteers who serve on artifact evaluation committees. It's a good step in the right direction.
And I'll also caution, again, that reasoning based on anecdotes can be distorted both ways. A tiny minority of people can claim the sky is falling and make it sound like Science is threatened by crisis. Those voices get amplified by the media and the attention economy and get blown out of proportion. What Science and institutions need is mechanisms that are fair and well-resourced and taken seriously. Those mechanisms and the people who volunteer time and effort to keep them running are the real heroes of Science, and breathless articles and anonymous anecdotes on the internet do not make up an accountability system.
If you see fraud, then REPORT IT to someone who can actually hold them accountable. Unsubstantiated and undirected accusations just spread distrust and do not increase accountability.
>And I'll also caution, again, that reasoning based on anecdotes can be distorted both ways...
I will go further and say that if you can clearly see how the incentives are aligned, even anecdotes are not necessary.
>If you see fraud, then REPORT IT to someone who can actually hold them accountable. Unsubstantiated and undirected accusations just spread distrust and do not increase accountability.
Mmmm..are you..for real? I mentioned incentives just above, so let us look at it. What is the incentive of this "person who can hold them accountable"? Do they really want to cut down the "output" of the institution by insisting on 100% honest? Do they really want to lose to competition which might allow such practices?
I think not. I think if this is reported, chances that the report will sit in some shelves or end up in some dust bin doing nothing. And the person who report it would suffer one way or the other.
yeah - skeptical here. Among certain departments, at large schools, under certain leaders.. The combination of "my marriage almost crumbled" for motivated reasoning, and "I have never seen any of this before" total inexperience with actual process.. the post shows itself to be biased and unreliable.
However, among certain departments, at large schools, under certain leaders.. yes, and growing
That's approximately 1 million people. Even a religious cult that size would have difficulty controlling motivations. As an example:
> Petitioners also formed a variety of organizations to create what they termed "marketable science." Pet. App. 1687a. For example, through the Council for To bacco Research (CTR) and Lawyers' Special Accounts, petitioners jointly financed research programs that were directed by company lawyers and calculated to yield favorable results. Id. at 240a-275a. Petitioners regu larly cited the conclusions of the scientists funded through these programs as if they were the objective results of disinterested research, without revealing that the scientists had, in fact, been funded by the industry. Id. at 195a.
I'm sure there's some, but the small point here is that it almost certainly is more motivated by factors other than financial gain. I'm sure it you search you can find such cases though.
The much broader point though is the dismissal of the bulk consensus of academic research because academics are in it for the "money".
Also, "science" isn't some sort of dogma that you should trust, it's a process you should follow.
"Trust the science" is anathema to the process. If anything, the chant should be "Doubt the science! Give it your best shot, refute it with data, with logic, provide a better explanation!"
Realistically, the vast majority of people will not have a real chance to "refute" or even evaluate scientific claims. Maybe given a lot of time and foundational work to learn the field, some percentage of people can usefully think about them, but the vast majority can't. A lot of people are functional illiterates. They will pick based on trust and gut feelings either way.
For example, when deciding whether to give your kids certain vaccines or not, you really can't expect that new parents will read the primary literature and try to refute or confirm the conclusions based on the numbers and will trace through the citations and so on... Any of those claims will also have some online account on social media refuting it with equally scientifically sounding words. In the end it will come down to heuristics and your model of how the world works, which set of people operate with what kind of intention. Like maybe you know people working in the field who you trust and hear from them that generally this sort of stuff can be trusted. Or maybe you had some bad experiences getting screwed by "the establishment" (maybe even unrelated to medicine) and now you lump all this together and distrust them.
Which is why we need people "doing science" to also focus on getting rid of bad ideas rather than just coming up with more. The present incentive structure is such that we reward people for coming up with shocking new ideas even if they are obviously rubbish and don't do enough to reward the ones who put in the effort to debunking existing bad ideas.
Coming up with ideas is the easy part of science, but most new ideas are wrong. Getting rid of the ones that aren't actually correct is hard, yet we shower praise on people doing the easy part and ignore the ones doing the hard part.
More broadly, an incredible amount of our society's systems are built around actors being uncoordinated. Redesigning institutions to resist networks of coordinated action between seemingly unlinked individuals will, in my opinion, be one of the great social challenges of this era.
This is the part that feels hardest to fix: once a system starts rewarding throughput over scrutiny, fraud stops looking like individual misconduct and starts looking like a supply chain problem.
One approach is more integration of researchers with businesses. Fraud (or simple incompetence) by researchers negatively affects businesses, as they expend effort on things that aren't real. I understand this is a constant problem in the pharmaceutical industry.
It's quite possible to be very successful marketing and selling things that aren't real. The market consists of humans, not perfectly rational machines.
firstly, there are basically no legal repercussions for scientific misconduct (e.g. falsifying data, fake images, etc.). most individuals who are caught doing this get either 1) a slap on the wrist if they are too big to fail or in the employ of those who are too big to fail or 2) disbarred, banned, and lose their jobs. i don't see why you can go to jail for lying to investors about the number of users in your app but don't go to jail for lying to the public, government, and members of the scientific community about your results.
secondly, due to the over production of PhD's and limited number of professorship slots competition has become so incredibly intense that in order to even be considered for these jobs you must have Nature, Cell, and Science papers (or the field equivalent). for those desperate for the job their academic career is over either way if they caught falsifying data or if they don't get the professorship. so if your project is not going the way you want it to then...
sad state of things all around. i've personally witnessed enough misconduct that i have made the decision to leave the field entirely and go do something else.
The future of science, the Internet, and all things: The Library of Babel by Jorge Luis Borges.
Some things should not have been democratized. Silicon Valley assumes that removing restrictions on information brings freedom, but reality shows that was naïve.
The Library of Babel comparison is too fatalistic imo, even granting that it's maybe just an extreme example. The real world doesn't quite resemble a closed system with no metadata. We can still establish chains of trust.
Whether or not people will build resilient chains is another story, contingent on whether the strength of that chain actually matters to people. It probably doesn't for a lot of people. Boo. But inasmuch as I care, I feel I ought to be free to try and derive a strong signal through the noise.
In what way was it was democratised? We're not talking about Substacks and YouTube channels here, we're not even talking about arXiv preprints and the like, we're talking about peer-reviewed journal publications, and that system remains gated in much the same way that it was in the 1980s when it comes to trying to publish in it. If anything this system is the poster child for top-down gatekeeping by the recognised authorities, and it's precisely the value of that official recognition that makes people so desperate to break into it. The major changes seem to have been the easy availability of author publication lists and the advent of publication metrics, not things which have been or were ever meant to be particularly democratising for would-be authors; and an increase in the number of people playing the game, driven to a large extent by increasing participation from developing countries, and hopefully not many people would have the gall to argue for a ban on developing-country participation.
there is no 'sin of maximal inclusivity here', the gate is broken, but primarily because it was largely an honor system before, and no one has the motivation or resources to really dig into a lot of these papers.
in no sense was it corrupted by the desire to include a larger population in journal publications.
There is a saying in my native language that goes something like "If you mix poison and milk, the milk will turn poisonous, instead of poison becoming milk (aka beneficial)".
I guess, to convert it into this context, we can say that if you mix the high minded and infantile (which I think is what Internet and social media did), the high minded becomes infantile, instead of the other way around.
Almost as if capitalism makes everything into a market, and the profits make it self sustaining.
How many will see the connections between this and our capitalist mode of production? Probably few since modern lit/news is allergic to systemic analysis.
The blatant flaws of capitalism can't be ignored for much longer.
All people in my extended family were Soviet scientists and engineers from multiple fields, and outside of experimental physics it was the same or worse. Same publish or perish pressure, same amount of fraud and lack of reproducibility. A ton of papers were made up. My father's lab lead was an absolute fraud (biochemistry), everybody knew that, and my father was unable to speak up until the late 90's.
When I was a kid I thought it was the issue with USSR rotting to the core (it was), but when it crashed and later when the web appeared, it became obvious that it's a common problem with academia and its incentives.
The only way to curb something like that is more democratization. Fraud is a common problem in any system anywhere. A reputation score on top would help, only if it can be kept democratically controlled.
There is no single solution, but public fund usurping is basically a law of capitalism, which is why I critique it in this context. Public money laundering is a developed industry in capitalism.
What I get from this is that the professional academic community -- as a whole -- has hit critical mass, which has produced a cottage industry of paper mills and fraudulent services to support said surplus.
Socialism wouldn't be the answer to this because socialism is famous for struggling with surpluses and shortages. All socialism would do is clamp down (hard) on academic's, which case you wind up with the famous shortage where not enough PHD's are available to produce research for an industry.
And that's not a problem specific to just socialism, that's the fallacy of central-planning. The US government clamped down on welfare fraud and the result were freak government social workers sniffing people's bed sheets and rooting through drawers and forcing everyone to document partners.
This is the situation where there needs to be a market correction because the alternative could be far worse.
It's the tax-payer funded business model, the NGO trap. Subsidies, grants, tax-breaks, credit, deductions, exemptions, etc. A whole class of profiteers live in this sector. Even though academia funding isn't strictly categorized as an NGO, it still fits/foots the bill. Public funding of private gains is the oldest trick in the book. Ask any capitalist, they know. And I'm not saying I'm against public funding, but this is often codified into a mafia of sorts when enough money flows through.
The real problem here is the fundamental lack of democratic control over our agencies. That our political organization is intensely lagging behind our productive organization. That our whole political will involves TRUSTING strangers to not be corrupt instead of directly democratizing these processes as much as possible.
But besides that, you cannot remove history from historical analysis. The reason socialism countries struggled in the beginning wasn't an inherent flaw in its organization, but the fact that they were under constant war war by capitalist countries through out their existence. Also keep in mind that most socialist countries did NOT have a whole section of the world where-from to extract riches through murder (S.America, Africa, Middle east, etc), like western capitalist countries had. This is convenient for you to ignore. Maybe because you don't know, or don't care about the super-exploitative history of these places and how they tie into western capitalism. But they are inherent to western wealth and these countries' whole history is struggle against this exploitation.
Not to mention that most of the countries on earth are capitalists and are very very very poor.
To add: Socialism has nothing to do with "clamping down" on X or Y industry, as you hypothetically claim would happen. Socialism is almost exclusively about removing the need to generate capital from production. It unleashes production from its historical ball and chain that is profiteering.
In a single sentence: Instead of production being held back by capitalists generating wealth we can produce for our own needs. It is self sustaining production.
Central planning is not fallacious. Your problem is with corruption, not democratic central planning. The US Govt is a pro-capitalist entity that pro-capitalists try to distance themselves from (ironically). So using them as an example isn't saying anything at all.
Central planning is not "allow a small group of people to decide things", as happens in the US Govt. Central planning is to take into account all sources of information on production to plan said production democratically.
This will always beat the highly highly inefficient speculation of capitalism. Where trillions vanish on a whim and cause of a tweet, where crisis occur every 8-10 years, and where its whole trade market is built to hide that it is mostly insider trading. Again, your problem is with corruption not democratic central planning.
And the way to deal with corruption is to create more democratic bodies where avg people hold real power. I don't see you asking for that either. We call that socialism.
Capitalism certainly is hugely flawed and yet it is far less flawed than any other economic system we know of. Experimentation with the foundations of society is about as risky as it gets. You could end up with a utopia or you could end up with another USSR. History tells us which outcome is more likely
People say that because they are not aware of what Marxism is.
Marxism isnt "lets try something different based on ideas of justice"
Marxism is "Society evolves through general stages: primitive, slave, feudal and capitalist, this is determined by the level production. Capitalism is holding back its current evolution into a society of plenty."
Good luck backing speculation and profit gatekept production.
It kinda skips over how large mainstream journals, with their restrictive and often arbitrary standards, have contributed to this. Most will refuse to publish replications, negative studies, or anything they deem unimportant, even if the study was conducted correctly.
So much of this started with the rise of the peer-review journal cartel, beginning with Pergamon Press in 1951 (coincidentally founded by Ghislaine Maxwell's father). "Peer review" didn't exist before then, science papers and discussion was published openly, and scientists focused on quality not quantity.
I'm not sure that the system was ever that near to perfection: for example, John Maddox of Nature didn't like the advent of pre-publication peer review, but that presumably had something to do with it limiting his discretion to approve and desk-reject whatever he wanted. But in any case it (like other aspects of the cozy interwar and then wartime scientific world) could surely never have survived the huge scaling-up that had already begun in the post-war era and created the pressure to switch to pre-publication peer reivew in the first place.
A deeper issue from the post-war era and science according to Dr. David Goostein, then vice-provost of Caltech from 1994: https://web.archive.org/web/20240213233731/https://www.its.c...
The paper may have a point in that the internet makes possible a certain scale of deception via paper mills and brokers and such -- but the motivation to use the internet that way comes from the growing financial pressures that Dr. Goodstein identified.Interesting. This is also freely available as PDF, by the way: https://doi.org/10.1029/97EO00213
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Peer review existed before 1951 in the US at least. See for example Einstein’s reaction to negative reviews when he tried to publish in Physical Review in 1935 https://paeditorial.co.uk/post/albert-einstein-what-did-he-t...
> coincidentally founded by Ghislaine Maxwell's father
A crazy world we live in where Robert Maxwell's daughter is more notorious than he is.
Fun fact, he almost got the worldwide console rights to Tetris back in the 80s, and tried going to Soviet officials to get those rights. To the point he's the antagonist of a recent "Tetris" movie that came out.
This is a fun fact, thank you.
Never knew of the guy but what a terrible sounding person from his Wikipedia at least.
Shit apple doesn’t fall far from the shit tree I guess.
100% this.
What is currently called "peer review" didn't exist back then, back then the meaning of "peer review" was just the back and forth happening in the open academic literature. Note the inevitable lack of finality in the original concept of peer review, a discussion in the scientific community could go on for 100's of years before being finally resolved. The current concept of "peer review" is closer to the concept of a delegation of some opaque ministry of truth composed of some opaquely selected experts (who often truly intend well) to settle in a short duration the finality.
Some measurements or experiments or questions to be settled can be very actionable and provide highly accurate results, others require much longer gathering of data to draw a clear picture.
The modern concept of "peer review" tries to sell the idea of almost immediate finality, like an economic transaction. In reality it is selling just the illusion, and creating lots of victims ranging from truth, individuals, departments institutions, or even entire fields (think of the replication crisis in psychology) along with any patients or others they treat.
>Pergamon Press in 1951 (coincidentally founded by Ghislaine Maxwell's father)
perhaps a bit off-topic, but what is coincidental about this and/or what is the relevance of Ghislaine Maxwell here?
It's useless, but I'm ashamed to admit I found this tiny piece of trivia interesting.
Like the paywall blocking many scientific arti6, perhaps it would be best if we released also the Epstein Files?
I believe by saying it is coincidental they are saying there is probably no relevance, just an interesting piece of trivia, why put out this interesting piece of trivia? Because maybe someone will be able to make an argument of relevance.
It's more than coincidental, but tangential to the point. It shows crime runs in families.
Ghislaine's father (Robert Maxwell) was also a terrible person but for different reasons.
Robert Maxwell was a crook, he used pension funds (supposed to be ring-fenced for the benefit of the pensioners) to prop up his companies, so, after his slightly mysterious death it was discovered that basically there's no money to pay people who've been assured of a pension when they retire.
He was also very litigious. If you said he was a crook when he was alive you'd better hope you can prove it and that you have funding to stay in the fight until you do. So this means the sort of people who call out crooks were especially unhappy about Robert Maxwell because he was a crook and he might sue you if you pointed it out.
I imagine it's the interesting peculiarity that the same people seem to crop up over and over and over again. Six degrees of Kevin Bacon or something, except it's like one or two degrees. As George Carlin said, "it's a big club, and you ain't in it"
For example Donald Barr (father of twice-former US Attorney General Bill Barr) hiring college-dropout Jeffrey Epstein whilst headmaster at the elite Dalton School
Additional fun facts about Donald Barr: he served in US intelligence during WWII, and wrote a sci-fi book featuring child sex slaves
Also the Epstein-Barr virus causes Mono, the clone of .NET, which was created by Bill Gates, known associate of Epstein, whose father was president of the Washington State Bar Association. And you know who else works in Washington? Join the dots, people.
We call people who make connections like these "conspiracy theorists," until they're right, at which point we call them "right". And somewhere in between, if they manage to get a job, we call them "Simpsons writers."
>We call people who make connections like these "conspiracy theorists," until they're right
They are never right, but boy do they love to pretend they are.
They are never right, even when they are right; they are only right 50 years down the road....at which point no one cares..
This might be my fav HN comment ever. Well done!
If you want to know more about the history of Pergamon Press there's a great Behind the Bastards episode on Robert Maxwell (Ghislaine Maxwell's father) - who himself was a scumbag in a variety of ways that were entirely distinct from Ghislaine Maxwell's brand of scumbaggery - that covers this. Might even be a multipart episode - it's a while since I've listened to it, but I have a feeling it's at least a two parter.
"Coincidental" means random, with no causal connection being explicitly claimed. It just means that two things share some characteristic (such as being relatives.) The thing that is coincidental is that the person who founded the company being discussed is also the father of another person who current events have brought into prominence.
It's why you would say something like "more than coincidental" if you were trying to make some causal claim, like one thing causing the other, or both things coming from the same cause.
So, "What is coincidental about that?" is a weird question. It reads as a rhetorical claim of a causal connection through asking for a denial or a disproof of one.
sorry.
what is the relevance to the discussion about journals and peer review is my main question.
if i randomly mentioned that your name appears to be an alternate spelling of a 3-band active EQ guitar pedal, coincidentally sharing all of the letters except one, in my reply to you, most people would be confused. that is how i felt when randomly reading "Ghislaine Maxwell" in this context of journals and peer review.
People have always been fascinated by where they might find monsters. This one so happens to be in our history, so many will find it noteworthy.
I wish you had highlighted or bolded "cartel", which is exactly how those industry players act.
>scientists focused on quality not quantity.
I know a PhD professor doing post doc or something, and he accepted a scientific study just because it was published in Nature.
He didn't look at methodology or data.
From that point forward, I have never really respected Academia. They seem like bottom floor scientists who never truly understood the scientific method.
It helped that a year later Ivys had their cheating scandals, fake data, and academia wide replication crisis.
When I read something in a textbook I blindly believe it, depending on the broader context and the textbook in question. Is that a bad thing?
People are constantly filtering everything based on heuristics. The important thing is to know how deep to look in any given situation. Hopefully the person you're referring to is proficient at that.
Keep in mind that research scientists need to keep abreast of far more developments than any human could possibly study in detail. Also that 50% of people are below average at their job.
There is a vast difference between a student reading from a textbook and a researcher / scientist reading studies and/or papers.
As a student you are to be directed* in your reading by an expert in the field of study that you are learning from. In many higher level courses a professor will assign multiple textbooks and assign reading from only particular chapters of those textbooks specifically because they have vetted those chapters for accuracy and alignment with their curriculum.
As a researcher and scientist a very large portion of your job is verifying and then integrating the research of others into your domain knowledge. The whole purpose of replicating studies is to look critically at the methodology of another scientist and try as hard as you can to prove them wrong. If you fail to prove them wrong and can produce the same results as them, they have done Good Science.
A textbook is the product of scientists and researchers Doing Science and publishing their results, other scientists and researchers verifying via replication, and then one of those scientists or researchers who is an expert in the field doing their best to compile their knowledge on the domain into a factually accurate and (relatively) easy to understand summary of the collective research performed in a specific domain.
The fact is that people make mistakes, and the job of a professor (who is an expert in a given field) is to identify what errors have made it through the various checks mentioned above and into circulation, often times making subjective judgement calls about what is 'factual enough' for the level of the class they are teaching, and leverage that to build a curriculum that is sound and helps elevate other individuals to the level of knowledge required to contribute to the ongoing scientific journey.
In short, it's not a bad thing if you're learning a subject by yourself for your own purposes and are not contributing to scientific advancement or working as an educator in higher-education.
* You can self-study, but to become an expert while doing so requires extremely keen discernment to be able to root out the common misconceptions that proliferate in any given field. In a blue-collar field this would be akin to picking up 'bad technique' by watching YouTube videos published by another self-taught tradesman; it's not always obvious when it happens.
> There is a vast difference between a student reading from a textbook and a researcher / scientist reading studies and/or papers.
Not really. Both are learning new things. Neither has the time or access to resources to replicate even a small fraction of things learned. Neither will ever make direct use of the vast majority of things learned.
Thus both depend on a cooperative model where trust is given to third parties to whom knowledge aggregation is outsourced. In that sense a textbook and prestigious peer reviewed journals serve the same purpose.
Papers in any journal (even or especially Nature, depending on your prior) should have a significantly larger degree of skepticism shown towards them than statements in reputable textbooks (which also should not be taken as complete gospel). Papers are a 'hey, we did a thing once, here's what we think it means' from a source that is very strongly motivated to do or find something novel or interesting, even if you trust that there is no fraud they are not something to approach uncritically.
> If you fail to prove them wrong and can produce the same results as them, they have done Good Science.
Not really in my humble opinion. Sure, the Popperian vibe is kind of fundamental, but the whole truncation into binary-valued true/false categories seldom makes sense with many (or even most?) problems for which probabilities, effect sizes, and related things matter more.
And if you fail to replicate a study, they may have still done Good Science. With replications, it should not be about Bad Science and Good Science but about the cumulation of evidence (or a lack thereof). That's what meta-analyses are about.
When we talk about Bad Science, it is about the industrial-scale fraud the article is talking about. No one should waste time replicating, citing, or reading that.
This is a good point. It is not humanly possible to verify every claim you read from every source.
Ideally, you should independently verify claims that appear to be particularly consequential or particularly questionable on the surface. But at some point you have to rely on heuristics like chain of trust (it was peer reviewed, it was published in a reputable textbook), or you will never make forward progress on anything.
> When I read something in a textbook I blindly believe it, depending on the broader context and the textbook in question. Is that a bad thing?
It is if what you read is factually incorrect, yes.
For example, I have read in a textbook that the tongue has very specific regions for taste. This is patently false.
> Keep in mind that research scientists need to keep abreast of far more developments than any human could possibly study in detail. Also that 50% of people are below average at their job.
So, we should probably just discount half of what we read from research scientists as "bad at their job" and not pay much attention to it? Which half? Why are you defending corruption?
The problem is that you can't just verify everything yourself. You likely have your own deadlines, and/or you want to do something more interesting than replicating statistical tests from a random paper.
> The problem is that you can't just verify everything yourself.
So the problem is reduced to "I believe what I want! This person said it and so I think it's true!"
Sounds like politics in a nutshell.
No, it's not. It's reduced to "I trust people from a respectable scientific journal with 150 years of history".
> Sounds like politics in a nutshell.
Again, no. It sounds like the division of labor. The thing that made modern human societies possible.
Division of labor. Dividing labor between the "i'll pay you to work" and "I'm paid to work"
The jokes write themselves,
Yes? What is exactly funny here? This is literally how the civilization works. I'm paid to do my work, and I pay others to do their work.
Do you grow your own food and sew your own clothes? Also, did you personally etch the microprocessor that runs your computer? The division of labor inherently means trusting others. So when I buy a bag of M4 screws, I'm not going to measure each screw with a micrometer, and I'm not taking X-ray spectra to verify their material composition.
The academic world also used to trust large publishers to take care to actually review papers. It appears that this trust is now misplaced. But I don't think it was somehow stupid.
>The problem is that you can't just verify everything yourself..
But one should be free to reject (in the sense that they should be free not to depend on it, or take decisions based on it) things that they did not themselves verify. But today if you do that, you become "anti-science"...and in-fact the people who wants others to "believe" science is anti-science..
The joke times we live in..
You don't seem to be engaging in good faith.
Most of the times you don't "accept" results. You have to build something on them, like an extension or a similar version on other field. So usually the first step is try to understand the cryptic published version and do a reproduction or something as close as possible.
The exact reproductions is never published, because journals don't accept them, but if you add a few tweaks here and there you have a nice seed for an article to publish somewhere.
(I may "accept" an article in a field I don't care, but you probably should not thrust my opinion in fields I don't care.)
Academia has problems, like everywhere else. But that seems like a big extrapolation from just one professor.
Fake data—you can only get that type of scandal when people are checking the data. I’d be more skeptical of communities that never have that kind of scandal.
In this case, the problem is a bit easier to identify and solve. Specifically, the Q-rating and publish or perish system is at fault. That can be fixed, or at least improved. Maybe we should be doing that instead of denying the obvious problems.
Hey man, trust the science.
Some "fun" reading on the subject of Mr. Maxwell:
https://sarahkendzior.substack.com/p/red-lines
tl;dr He is the bridge that uncomfortably links Biden's former Secretary of State, Antony Blinken, to Jeffrey Epstein and Mossad. Hence, *gestures at the last couple of weeks and years*. Dude was just, like, Fraud Central, apparently.
Plenty will publish it, but those are not as highly regarded by the community. It's not a problem of journals. It's not hard to start your own journal by teaming up with other academics. In machine learning, ICLR is such a venue for example. The problem is much deeper and more fundamental. You want to publish alongside groundbreaking novel research. Researcher's own ears perk up when they hear about something new. They invite colleagues to talk about their novel discoveries not to describe all their null results and successful replications of known results. Funding agencies want research with novelty and impact. They want to write reports to the higher ups and the politicians and the donors that document the innovations that their funding brought. The media will republish press releases that have cool new results.
To have research happening, you need someone saying "I want to give money to this researcher". There is an endless queue of people lining up who are ready to take this money and do something with it. The person with money (govt or private) has to use some heuristics to pick. One way is to say "I trust this one, I don't care too much what the project is, I'm sure this person will do something that makes sense". But that is dependent on a track record.
Do you want issues of Nature and cell to be replication studies? As a reader even from within the field, im not interested in browsing through negative studies. It'll be great if I can look them up when needed but im not looking forward to email ToC alerts filled with them.
Also who's funding you for replication work? Do you know the pressure you have in tenure track to have a consistent thesis on what you work on?
Literally every single know that designs academia is tuned to not incentivize what you complain about. Its not just journals being picky.
Also the people committing fraud aren't ones who will say "gosh I will replicate things now!" Replicating work is far more difficult than a lot of original work.
> Do you want issues of Nature and cell to be replication studies?
Of course I do! Not all of course, and taking (subjectively measured) impact into account. "We tried to replicate the study published in the same journal 3 years ago using a larger sample size and failed to achieve similar results..." OR "after successfully replicating the study we can confirm the therapeutic mechanism proposed by X actually works" - these are extremely important results that are takin into account in meta studies and e.g. form the base of policies worldwide.
Honestly even if they didn't publish the whole paper, if there was just a page that was a table of all the replication studies that were done recently, that would be pretty cool.
> Do you want issues of Nature and cell to be replication studies?
More than anything. That might legitimately be enough to save science on its own.
Maybe nature and cell and a few other journals should be exceptions: they should be the place that the most advanced scientists publish interesting ideas early for the consumption by their competitors. At that level of science, all the competitors can reproduce each other's experiments if necessary; the real value is expanding the knowledge of what seems possible quickly.
(I am not seriously proposing this, but it's interesting to think about distinguishing between the very small amount of truly innovative discovery versus the very long tail of more routine methods development and filling out gaps in knowledge)
> that level of science, all the competitors can reproduce each other's experiments if necessary
But they don't, and that's the problem!
The problem is bigger. It even blocks research!
In my own experience I was unable to publish a few works because I was unable to outperform a "competitor" (technically we're all on the same side, right?). So I dig more and more into their work and really try to replicate their work. I can't! Emailing the authors I get no further and only more questions. I submit the papers anyways, adding a section about replication efforts. You guessed it, rejected. With explicit comments from reviewers about lack of impact due to "competitor's" results.
Is an experience I've found a lot of colleagues share. And I don't understand it. Every failed replication should teach us something new. Something about the bounds of where a method works.
It's odd. In our strive for novelty we sure do turn down a lot of novel results. In our strive to reduce redundancy we sure do create a lot of redundancy.
I've seen this from both sides.
Sometimes the result is wrong, or it's not as big or as general as claimed. Or maybe the provided instructions are insufficient to replicate the work. But sometimes the attempt to replicate a result fails, because the person doing it does not understand the topic well enough.
Maybe they are just doing the wrong things, because their general understanding of the situation is incorrect. Maybe they fail to follow the instructions correctly, because they have subtle misunderstandings. Or maybe they are trying to replicate the result with data they consider similar, but which is actually different in an important way.
The last one is often a particularly difficult situation to resolve. If you understand the topic well enough, you may be able to figure out how the data is different and what should be changed to replicate the result. But that requires access to the data. Very often, one side has the data and another side the understanding, but neither side has both.
Then there is the question of time. Very often, the person trying to replicate the result has a deadline. If they haven't succeeded by then, they will abandon the attempt and move on. But the deadline may be so tight that the authors can't be reasonably expected to figure out the situation by then. Maybe if there is a simple answer, the authors can be expected to provide it. But if the issue looks complex, it may take months before they have sufficient time to investigate it. Or if the initial request is badly worded or shows a lack of understanding, it may not be worth dealing with. (Consider all the bad bug reports and support requests you have seen.)
I definitely think all these are important, even if in different ways. For the subtle (and even not so subtle) misunderstandings it matters who misunderstands. For the most part, I don't think we should concern ourselves with non-experts. We do need science communicators, but this is a different job (I'm quite annoyed at those on HN who critique arxiv papers for being too complex while admitting they aren't researchers themselves). We write papers to communicate to peers, not the public. If we were to write to the latter each publication would have to be prepended by several textbooks worth of material. But if it is another expert misunderstanding, then I think there's something quite valuable there. IFF the other expert is acting in good faith (i.e. they are doing more than a quick read and actually taking their time with the work) then I think it highlights ambiguity. I think the best way to approach this is distinguish by how prolific the misunderstanding is. If it is uncommon, well... we're human and no matter how smart you are you'll produce mountains of evidence to the contrary (we all do stupid shit). But if the misunderstanding is prolific then we can be certain that ambiguity exists, and it is worth resolving. I've seen exactly what you've seen as well as misunderstandings leading to discoveries. Sometimes our idiocracy can be helpful lol.
But in any case, I don't know how we figure out which category of failures it is without it being published. If no one else reads it it substantially reduces the odds of finding the problem.
FWIW, I'm highly in favor of a low bar to publishing. The goal of publishing is to communicate to our peers. I'm not sure why we get so fixated on these things like journal prestige. That's missing the point. My bar is: 1) it is not obviously wrong, 2) it is not plagiarized (obviously or not), 3) it is useful to someone. We do need some filters, but there's already natural filters beyond the journals and conferences. I mean we're all frequently reading "preprints" already, right? I think one of the biggest mistakes we make is conflate publication with correctness. We can't prove correctness anywhere, science is more about the process of elimination. It's silly to think that the review process could provide correctness. It can (imperfectly) invalidate works, but not validate them. It isn't just the public that seems to have this misunderstanding...
Things are easier when you are writing to your peers within an established academic field. But all too often, the target audience includes people in neighboring fields. Then it can easily be that most people trying to replicate the work are non-experts.
For example, most of my work is in algorithmic bioinformatics, which is a small field. Computer scientists developing similar methods may want to replicate my work, but they often lack the practical familiarity with bioinformatics. Bioinformaticians trying to be early adopters may also try to replicate the work, but they are often not familiar with the theoretical aspects. Such a variety of backgrounds can be a fertile ground for misunderstandings.
Sure. You can't write to everyone and there's tradeoffs to broadening your audience. But I'm also not sure what your point is. That people are arrogant? Such variety of backgrounds can also be fertile ground for collaboration. Something that should happen more often
As a gross simplification, there are two kinds of fields. Some are defined by the methods they use, and some by the topics they study.
The latter will use any methods that may yield results. That creates a problem. The people who are in the target audience for a paper and may try to replicate the results often fail to do so, because they lack the expertise. Because their background is too different.
I think you think that because we don't agree that I have some grave misunderstanding of some, to be frank, basic facts. I assure you, I perfectly understand what you're bringing up here and in the last comment.
But I think you still haven't understood my point about trade-offs. At least you aren't responding as if these exist.
Our disagreement isn't due to lack of understanding the conditions, it is due to a difference in acceptable limitations. After all, perfection doesn't exist.
So you can't just solve problems like this by bringing up limitations in an opposing viewpoint. I assure you, I was already well aware of every single one you've mentioned...
My original point was that replication attempts often fail, because the person trying to replicate the result is not an expert in the field, and they do not have enough time to devote to the effort. This is a common situation in fields that use results from other fields. If they don't have the time for proper replication, they probably don't have the time for publishing the attempt.
As for your point, I don't really understand what you are trying to say.
Advanced groups usually replicate their competitor's results in their own hands shortly after publication (or they just trust their competitor's competence). But they don't spend any time publishing it unless they fail to replicate and can explain why they can't replicate. From their perspective, it's a waste of time. I think this has been shown to be a naive approach (given the high rate of image fraud in molecular biology) but people who are in the top of the field have strong incentives to focus on moving the state of the art forward without expending energy on improving the field as a whole.
"strong incentives to focus on moving the state of the art forward without expending energy on improving the field as a whole"
That sort of Orwellian doublethink is exactly the problem. They need to move it forward without improving it, contribute without adding anything, challenge accepted dogma without rocking the boat, and...blech!
You must create paradigm shifts without challenging the current paradigm!
[0] https://www.scientificamerican.com/article/katalin-karikos-n...
[1] https://www.globalperformanceinsights.com/post/how-a-rejecte...
"Science is the belief in the ignorance of experts" - Richard Feynman
All that makes it more important for top journals to reward replication, not less!
Top journals are not inherently prestigious. They are prestigious because they try to publish only the most interesting and most significant results. If they started publishing successful replication studies, they would lose prestige, and more interesting journals would eventually rise to the top. (Replication studies that fail to replicate a major result in a spectacular way are another matter.)
Are you explaining this from experience or from speculation?
I can tell you that it doesn't match my own experience. I also think it doesn't match your example. Those cases of verified image fraud are typically part of replication efforts. The reason the fraud is able to persist is due to the lack of replication, not the abundance of it.
Mostly experience (based on being a PhD scientist, a postdoc, a National Lab scientist, and engineer at several bigtech companies), partly speculation (none of the groups/labs I worked in operated at "the highest level", but I worked adjacent to many of those).
I'm pretty sure most image fraud went completely unrealized even in the case of replication failure. It looks like (pre AI) it was mostly a few folks who did it as a hobby, unrelated to their regular jobs/replication work.
In most of the labs I've worked in replication is not a common task[0]
Part of my point is that being unable to publish replication efforts means we don't reduce ambiguity in the original experiments. I was taught that I should write a paper well enough that a PhD student (rather than candidate) should be able to reproduce the work. IME replication failures are often explained with "well I must be doing something wrong." A reasonable conclusion, but even if true the conclusion is that the original explanation was insufficiently clear. I'm sorry, didn't you say Because your current statement seems to completely contradict your previous one.Or are you suggesting that the groups you didn't work with (and are thus speculating) are the ones who replicate works and the ones you did work with "just trust their competitor's competence")? Because if this is what you're saying then I do not think this "mostly" matches your experience. That your experience more closely matches my own.
[0] I should take that back. I started in physics (undergrad) and went to CS for grad. Replication could often be de facto in physics, as it was a necessary step towards progress. You often couldn't improve an idea without understanding/replicating it (both theoretical and experimental). But my experience in CS, including at national labs, was that people didn't even run the code. Even when code was provided as part of reviewing artifacts I found that my fellow reviewers often didn't even look at it, let alone run it... This was common at tier 1 conferences mind you... I only knew one other person that consistently ran code.
Note that my field is biophysics (quantitative biology) while yours is physics and CS. Those are done completely differently from biology; with the exception of some truly enormous/complex/delicate experiments that require unique hardware, physics tends to be much more reproducible than biology, and CS doubly-so.
Replication of an experiment and finding image fraud are kind of done as two different things. If somebody publishes a paper with image fraud, it's still entirely possible to replicate their results(!) and if somebody publishes a paper without any image fraud, it's still entirely possible that others could fail to replicate. Also, most image errors in papers are, imho, due to sloppy handling/individual errors, rather than intentional fraud (it's one of the reasons I worked so hard on automating my papers- if I did make an error, there should be audit log demonstrating the problem, and the error should be rectified easily/quickly in the same way we fix bugs in production at big tech).
This came up a bunch when I was at LBL because of work done by Mina Bissell there on extracellular matrix. She is actively rewriting the paradigm but many people can't reproduce her results- complex molecular biology is notororiously fickle. Usually the answer is, "if you're a good researcher and can't reproduce my work, you come to my lab and reproduce it there" because the variables that affect this are usually things in the lab- the temperature, the reagents, the handling.
See https://www.nature.com/articles/503333a (written by Dr. Bissell).
For CS the problem is wildly different. It should be easy to reproduce as code is trivial to copy. Ignoring the issue of not publishing code alongside results, there's also often subtle things that can make or break works. I've found many times in replication efforts that the success can rely on a single line that essentially comes form a work that was the reference to a reference of the work I'm trying to reproduce. The problem here is honestly more of laziness. In contrast to physics there's an extreme need for speed. In physics (like everyone else I knew) I often felt like I was not smart enough, and that encouraged people to dive deeper and keep improving or to give up. In CS (like everyone else I knew) I often felt like I was not fast enough, and that encouraged people to chase sponsorships from labs that provided more compute, it encouraged a "shotgun" approach (try everything), or for people to give up (aka "GPU poor").
The reason I'm saying this is because I think it is important to understand the different cultures and how replication efforts differ. In physics a replication failure was often assumed to be due to a lack of intelligence. In CS a replication effort is seen as a waste of time. Both are failures of the scientific process. Science is intended to be self-correcting. Replication is one means of this, but at its heart is the pursuit of counterfactual models. This gives us ways to validate, or invalidate, models through means other than direct replication. You can pursue the consequences of the results if you are unable to pursue the replication itself. This is almost always a good path to follow as it is the same one that leads to the extension and improvement of understanding.
There's a lot I agree and disagree with from Dr Bissell's article. Our perspectives may differ due to our different fields, but I do think it also serves as some a point of collaboration, if not on the subject of meta-science. Biology is not unique in having expensive experiments. I want to point out two famous and large physics projects: the LHC's discovery of the Higgs Boson[0] and LIGO's Observation of a Gravitational Wave[1]. The former has 9 full pages of authors (IIRC over 200) while the latter has about 3. These works are both too expensive to replicate while also demonstrating replication. Certainly we aren't going to take another 2 decades to build another CERN and replicate the experiments. But there's an easy to miss question that might also make apparent the existence of replication: who is qualified to review the paper and is not already an author of it? There's definitely some, but it really isn't that many. In these mega projects (and there are plenty more examples) the replication is done through collaboration. Independent teams examine the instruments that make the measurements. Independent teams make measurements, using the same device or different devices (ATLAS isn't the only detector at CERN), different teams independently analyze and process the information, and different teams model and simulate them. With LIGO this is also true. It would be impossible to locate those black holes without at least 2 facilities: one in Hanford (Washington) and the other in Livingston (Louisiana) (and now there's even more facilities). Astrophysics has a long history of this type of replication/collaboration as one team will announce an observation and it is a request for other observations. Observations that often were already made! In HEP (high energy particle physics) this may be less direct, but you'll notice other particle physics labs are in the author list of[0]. That's because despite the exact experiment not being replicatable in other facilities, there are still other experiments done. In the effort to find the Higgs there were many collisions performed at Fermi Lab.
I don't think this same in biophysics, but I think there are nuggets that may be fruitful. Bissell mentions at the end of her argument that she believes replication might have higher success were labs to send scientists to the original labs. I fully agree! That would follow the practice we see in these mega experiments in physics. But I also do think she's brushing off an important factor: it is far quicker and cheaper to replicate works than it is to produce them. You're a scientist, you know how the vast majority of time (and usually the vast majority of money) is "wasted" in failures (it'd be naive to call it waste). Much of this goes away with replication efforts. The greater the collaboration the greater the reduction in time and money.
And I do agree with Bissell in that we probably shouldn't replicate everything[2]. At least if we want to optimize our progress. But also I want to stress that there is no perfect system and there are many roadblocks to progress. Frankly, I'd argue that we waste far more time in things like grant writing and publication revisions. I don't know a single scientist who hasn't had a work rejected due to reviewers either not giving the work enough care or simply because they were unqualified (often working in a different niche so don't understand the minutia of the problem). As for the grant writings, I think they're a necessary evil but I'm also a firm believer of what Mervin Kelly (former director of Bell Labs) said when asked how you manage a bunch of geniuses: "you don't"[3]. You're a scientist, an expert in your domain. You already know what directions to look in. You've only gotten this far because you've been honing that skill. We don't have infinite money, so of course we have to have some bar, but we can already sniff out promising directions and we're much better at sniffing out fraud. Science has been designed to be self-correcting.
[More of a side note]
And we should not undermine the importance of these variables. Failures based on them are still informative. They still inform us about the underlying causal structure that leads to success. If these variables were not specified in the paper, then a replication failure shows the mistake of the writing. Alternatively a failure can bound these variables, by making them more explicit. I'm no expert in biophysics, but I'm fairly certain that understanding the bounds of the solution space is important for understanding how the processes actually work.[0] https://arxiv.org/abs/1207.7214
[1] https://arxiv.org/abs/1602.03837
[2] I also would be very cautious about paid replication efforts. I am strongly against it as well as paywalls on publishing (both in creation of publication as well as the access of).
[3] https://1517.substack.com/p/why-bell-labs-worked
Replication studies cannot save science and might make the fraud problem worse.
https://blog.plan99.net/replication-studies-cant-fix-science...
> Do you want issues of Nature and cell to be replication studies? As a reader even from within the field, im not interested in browsing through negative studies.
Actually, yes, I do. The marginal cost for publishing a study online at this point is essentially nil.
I think archives with pretty low standards for notability are a good idea. At some point though you have to pick what actually counts as interesting enough to go in the curated list that is actually suggested reading, where the prestige is attached. If there's no curation by Nature then it falls to bloggers or another journal to sift through the fire-hose and make best-of lists. Most of the value is in the curation, not the publishing. Without exclusivity there's very little signal.
> The marginal cost for publishing a study online at this point is essentially nil.
The marginal cost for doing a study remains the same, which is quite a bit. Society doesn't have unlimited scientific talent or hours. Every year someone spends replicating is a year lost to creating something new and valuable.
I know you got a ton of responses already but not caring about replicability just invalidates science as a method. If we care only about first to publish we end up in the current situation where we don't even know that we know is actually even remotely correct.
All because journals prefer novelty over confirmation. It's like a castle of cards, looks cool but not stable or long-term at all.
> Do you want issues of Nature and cell to be replication studies?
Hell yeah. We’re all trying to get that Nature paper. Imagine if you could accomplish that by setting the record straight.
If you're thoroughly debunking a previous Nature paper they just might publish that. But the expectation is that you'll succeed. Publishing that sort of mundane article would reduce the prestige of getting something into the journal. Publishing in a high impact journal is only seen as an achievement in the first place because of what it implies about the content of your paper.
[dead]
If you're a reader within the field, then you are the one person in the world who should be most interested in negative replication studies.
Realistically, everyone will say “yes” to the “do you want” question because if you’re not a reader or a subscriber you benefit from the readers reading replication studies.
I believe people will enthusiastically say yes but that they do not routinely read that journal.
Suggesting that people would stop reading Nature if they also included replication studies send like an incredible leap.
It would directly undermine the reason that people read Nature in the first place.
Not really.
"It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so."
Knowing that something I thought was true was actually false would have saved me years in several situations.
I didn't understand us to only be talking about failed replication studies of previous Nature papers which would hopefully be few and far between and thus noteworthy indeed. Rather replication studies in general which on average are arguably less interesting to the reader than even the content of the typical archival journal.
They certainly will be few and far between when the system is structured to repress them. But there's reason to believe they wouldn't be as rare as you seem to think:
https://www.nature.com/nature/articles?type=retraction
Are you seriously attempting to imply that Nature retractions aren't few and far between?
What's even your point here? Hopefully we are at least in agreement that Nature is seen as prestigious and worth looking through precisely because of the sort of content that they publish. Diluting that would dilute their very nature. (Bad pun very much intended sorry I just couldn't resist.)
"Are you seriously attempting to imply that Nature retractions aren't few and far between?"
No. I'm explicitly stating that they are few and far between, but perhaps (not certainly, but conceivably) they shouldn't be.
"What's even your point here?"
My point is that focusing on positive findings and neglecting negative findings perverts the mechanism that makes science work. Science isn't about proving things correct, it's about rooting out errors.
I'm not sure I agree. The system certainly isn't optimal but results aren't just dumped into a vacuum. Something is only useful if people can build on it. Even if negative results don't get published, even if it isn't optimal, by virtue of future positive results building on past things that did reproduce you get forward progress.
Regardless, I don't think that's at odds with my original assertion that becoming a venue for publishing negative results would undermine the "point" of Nature.
The missing link isn't a venue in which to publish. It's funding to do the work in the first place. Also funding to spend the time writing it up when you find that you've inadvertently been tricked into doing the work while trying to get something that builds on it to work.
"Also funding to spend the time writing it up when you find that you've inadvertently been tricked into doing the work while trying to get something that builds on it to work."
Oh there have been times would have loved to be able to apply for one of those!
That is a novel interpretation of my comment certainly.
Tagging seems like an option here.
"Original research" isn't worth much unless replicated, which is the entire problem being discussed in this thread. Replicating studies are great though because they tell you if the original research actually stands and is valid.
> Replicating work is far more difficult than a lot of original work.
Only if the original work was BS. And what, just because it's harder, we shouldn't do it?
Why blame just the journals when every other system also disintivizes the same.
I must be missing something, surely the argument isn't "other systems also disincentivize solving the problem, therefore we shouldn't work to fix this one"
Even if that negative study could save you one, two, three+ years of work for the same outcome (which you then also can't really do anything with)? Shouldn't there BE funding for replication studies? Shouldn't that count towards tenure? Part of the problem is that publications play such a heavy role in getting tenure in the first place.
I'm sure you can more narrowly tune your email alerts FFS.
> Do you want issues of Nature and cell to be replication studies?
There are journals dedicated to replication, like ReScience C[1]. But they are niche. Perhaps we should have more of these.
[1] http://rescience.github.io/
> Replicating work is far more difficult than a lot of original work.
I don’t regularly read scientific studies but I’ve read a few of them.
How is it possible that a serious study is harder to replicate than it is to do originally. Are papers no longer including their process? Are we at the point where they are just saying “trust me bro” for how they achieved their results?
> Do you want issues of Nature and cell to be replication studies?
Not issues of Nature but I’ve long thought that universities or the government should fund a department of “I don’t believe you” entirely focused on reproducing scientific results and seeing if they are real
> How is it possible that a serious study is harder to replicate than it is to do originally.
They aren't. GP was on point until that last sentence. Just pretend that wasn't there. It's pretty much always much easier to do something when all the key details have been figured out for you in advance.
There is some difficulty if something doesn't work to distinguish user error from ambiguity of original publication from outright fraud. That can be daunting. But the vast majority of the time it isn't fraud and simply emailing the original author will get you on track. Most authors are overjoyed to learn about someone using their work. If you want to be cynical about it, how else would you get your citation count up?
>Also who's funding you for replication work? Do you know the pressure you have in tenure track to have a consistent thesis on what you work on?
This is partly why much of today's science is bs, pure and simple.
I have worked in this particular sausage factory. Multiple funded random replications are the only thing that will save science from this crisis. The scientific method works. We need to actually do it.
Replications don't have to be in the journals either. As long as money flows, someone will do them, and that is what matters. The randomization will help prevent coordination between authors and replicators.
In a better world, negative studies and replications would count towards tenure, but that is unlikely to occur. At least half of the problem is the pressure to continuously publish positive results.
Regardless of what gets taught in school about science being objective and without ego, or having a culture of adversarial checks on each other etc., the reality is that scientists are humans and have egos and have petty feuds.
Publishing a failed replication of the work of a colleague will not earn you many brownie points. I'm stating this as an observation of what is the case, not as something that I think should be the case. If you attack other researchers like this and damage their reputation - even if for valid scientific reason - you'll have a hard time when those colleagues sit on committees deciding about your next grant etc.
Of course if you discover something truly monumental that will override this. But simply sniping down the mediocre research published by other run-of-the-mill researchers will get you more trouble than good. Yes it's directly in contradiction to the textbook-ideal of what science should be, as described to high school students, but there are many things in life this way.
Of course it can be laudable to go on such a crusade despite all this, and to relentlessly pursue scientific truth, etc. but that just won't scale.
You are absolutely correct. Even distributing the replication around the world will only help so much. It's a small world out there and only smaller in the specializations.
That's why replication has to be required and standard. It will hurt to tear off the bandaid, but once the culture shifts, people will hesitate to publish mediocre research in the first place. Without mediocre research flooding the zone, real numbers will dominate and inflated expectations will wither.
> That's why replication has to be required and standard.
"has to be required"... This is a passive construct. Who will do the requiring and what precisely will motivate them to such a change and what will get them the buy-in from the other players in this whole ecosystem, especially the ones who provide the money? What if it turns out that those people who do the funding actually in the deepest of their deepest are fine with "groundbreaking" research results that simply sound like being "groundbreaking" research results to such an extent that their prestige and social status rises enough and are seen as someone who funds such research, instead of truly caring about the actual contents of said research? There is much more demand (backed with money) for (plausibly-claimable) innovation and breakthroughs than supply of real novel thought. It's a bit like the anecdote that all the True Cross relics across Catholic churches weigh more than the cross Jesus carried (not really true as a fact though). As long as there is such strong demand, the system will adapt to allow for the supply finding its way.
Maybe we need a journal completely dedicated to replication studies? It would attract a lot of attention I think.
Economics has the Journal of Comments and Replications in Economics: https://jcr-econ.org/
And funding dedicated to replication studies.
paid by the original authors if their study fails to replicate
In the US such a requirement would violate the 1st amendment. It's also probably counterproductive.
punishing failure is probably counterproductive? let me guess, participation trophies is the way...
Let me introduce you to the concept of "unintended consequences".
We already have archival journals. What's missing is funding and any prospect of career advancement.
Is there a viable career path for researchers who choose to focus on replication instead of novel discoveries? I assume replications are perceived as less prestigious, but it's also important work.
The closest thing we have is, in security / privacy / cryptography, you can write "attack" papers.
It's not perfect. You don't get any credit unless you can demonstrate a substantial break of the prior work. But it's better than in a lot of other fields.
sadly no, this is not a thing and it's critically needed.
top on my list of things to do if i were a billionaire: launch an institute for the sole purpose of reproducing other's findings.
Mainstream journals are complicit, but are not the biggest problem.
The biggest problem by far is modern society: Tenure, getting paid a livable wage as a researcher, not getting stack-ranked and eliminated from your organization all overindex on positive research results that are marketable. This "loss function" encourages scientific fraud of sorts.
When, in those mythical non-"modern" times, was it easy to get tenure or a livable wage as a researcher? How open were the doors to this and what proportion of society got a realistic chance to pursue such a career? More people getting a chance means more fierce competition.
One major contributing factor, in my opinion, is that almost no one in the community was taught the scientific method / epistemology itself.
The simple fact that theories should be falsified and not verified is something that most scientists don’t know.
This isn’t about honest researchers resorting to fraud to publish their null results because they were blocked by big bad Nature. It’s about journals and authors churning out pure junk papers whose only goal is to game metrics like citation count.
Right, it seems that many of the weaknesses in the system exist because they serve the interests of journal publishers or of normal, legitimate-ish researchers, but in the process open the door to full-time system-hackers and pure fraudsters.
Any system that grows too fast has these kinds of problems. When it's a small intimate circle where everyone knows everyone, reputation alone can keep people in check. Once it's larger you need to invent rules and bureaucracies and structures and you will have loopholes that bad actors can more easily exploit, hiding in the crowd, than in the small version. It's the same with the Internet or computing. Security was much less of a topic when it was mostly honest academic nerds using the Internet, and the protocol designs often didn't even assume adversarial participants. Science also still runs on this assumed honesty system that worked well when it was small.
Yes, as long as (for example) you have a huge number of people desperate to claim one or more of a very small number of slots for papers in a few prestige journals then inevitably there must be either a cattle-call selection process, or blatant preference for existing insiders, or both. But things like the fancy journals' reluctance to acknowledge or care about errors in the papers they do publish really aren't at all a necessary consequence of a high submission ratio or of a rapid growth in submissions. In fact it's not even clear that they are a consequence of those pressures at all, let alone that they do anything to mitigate them. They're just things which serve the interests of insiders while in fact making the consequences of the Darwinian pressures worse.
The publish or perish culture leads to this. The businessmen and politicians should never be allowed to decide academic fundings.
I don't know what the solution is, but I do know that our fear of people wasting money and creating fraudulent studies has only resulted in wasting money and fraudulent studies. We've removed the verification system while creating strong incentives to cheat (punish or perish, right?).
I think one thing we do need to recognize is that in the grand scheme of things, academia isn't very expensive. A small percentage of a large number is still a large number. Even if half of academics were frauds it would be a small percentage of waste, and pale in comparison to more common waste, fraud, and abuse of government funds.
From what I can tell, the US spent $60bn for University R&D in 2023[0] (less than 1% of US Federal expenditures). But in that same time there was $400bn in waste and fraud through Covid relief funds [1]. With $280bn being straight up fraud. That alone is more than 4x of all academic research funding!!!
I'm unconvinced most in academia are motivated by money or prestige, as it's a terrible way to achieve those things. But I am convinced people are likely to commit fraud when their livelihoods are at stake or when they can believe that a small lie now will allow them to continue doing their work. So as I see it, the publish or perish paradigm only promotes the former. The lack of replication only allows, and even normalizes, the latter. The stress for novelty only makes academics try to write more like business people, trying to sell their product in some perverse rat race.
So I think we have to be a bit honest here. Even if we were to naively make this space essentially unregulated it couldn't be the pinnacle of waste, fraud, and abuse that many claim it is. But I doubt even letting scientists be entirely free from publication requirements that you'd find much waste, fraud, and abuse. Science has a naturally regulating structure. It was literally created to be that way! We got to where we are in through this self regulating system because scientists love to argue about who is right and the process of science is meant to do exactly that. Was there waste and fraud in the past? Yes. I don't think it's entirely avoidable, it'll never be $0 of waste money. But the system was undoubtably successful. And those that took advantage of the system were better at fooling the public than they were their fellow scientists. Which is something I think we've still failed to catch onto
[0] https://usafacts.org/articles/what-do-universities-do-with-t...
[1] https://apnews.com/article/pandemic-fraud-waste-billions-sma...
You either have something documented and quantified and measured and objective criteria tickboxes and deal with this style of failure mode, or you rely on subjective judgment and assessment and accept the failure mode of bias, nepotism, old boy's clubs etc. Of course the ideal case is to rely on the unbureaucratic informal wise and impartial judgment of some hypothetical perfect humans you can fully trust and rely on, and they always decide fully on merits etc. without having to follow any rigid criteria and checkboxes and numbers on hiring and promotion etc. But people are not perfect and society largely decided to go the bureaucratic way to ensure equal opportunities and to reduce bias through this kind of transparency.
You have a fundamental flaw in your argument, one that illustrates a common, yet fundamental, misunderstanding of science. There is no "objective" thing to measure, there are only proxies. I actually recently stumbled on a short by Adam Savage that I think captures this[0], although I think he's a bit wrong too. Regardless of precision we are always using a proxy. A tape measure does not define a meter, it only serves as a reference to compare with. A reference where not only the human makes error when reading, but that the reference itself has error[1]. So there are no direct measurements, there are only measurements by proxy.
You may have heard someone say "science doesn't prove things, it disproves them", and that's in part a consequence to this. Our measurements are meaningless without an understanding of their uncertainty (both quantifiable and unquantifiable!) as well as the assumptions they are made under.
I'm not trying to be pedantic here, I think this precision in understanding matters to the conversation. My argument is that by discounting those errors that they accumulate. We've had a pretty good run. This current system has only really started to be practiced in the 60s and 70's. So 50 years is a lot of time for error to accumulate. 50 years is a lot of time for small, seemingly insignificant, and easy to dismiss errors to accumulate into large, intangible, and complex problems.
There's something that I guess is more subtle in my argument: science is self-correcting. I don't mean "science" as the category of pursuits that seek truths about the world around us, but I mean "science" as a systematic approach to obtaining knowledge. A key reason this self-correction happens is due to replication. But in reality that is a consequence of how we pin down truth itself. We seek causal structures. More specifically, we seek counterfactual models. Assuming honest practitioners, failures of reproduction happen for primarily for one of two reasons: 1) ambiguity of communication between the original experimenters and those replicating or 2) a variation in conditions. 2) is actually quite common and tells us something new about that causal structure. In practice it is extremely difficult, if not impossible, to exactly replicate the conditions of the original experiment, so even with successful replication we gain information about the robustness of the results.
But why am I talking about all this? Because without the explicit acknowledgement of these limitations we seem to easily forget them. We are often treating substantially more subjective measures (such as impact or novelty) as far more objective than we would treat even physical measurements. It should be absolutely no surprise that things like impact are at best extremely difficult to measure. Even with a time machine we may not accurately measure the impact of a work for decades, or more. Ironically, a major reason for a work's impact to be found only after decades (or centuries) is the belief that at its time it had no impact, and was a dead end. You'd be amazed at how common this actually is. It's where jokes similar to how everything is named after the second person to discover something, the first being Euler[2]. But science is self-correcting. Even if a discovery of Euler's was lost, it is only a matter of time before someone (independently) rediscovers it.
I'm talking about this because there is no perfect system. Because a measurement without the acknowledge of its uncertainty is far less accurate than a measurement with. I'm talking about this because we will always have errors and the existence of them is not a reason to dismiss things. Instead we have to compare and contrast both the benefits and limits of competing ideas. We are only doing ourselves a disservice by pretending the limits don't exist. And if we mindlessly pursue objective measurements we'll only end up finding we've metric hacked our way into reading tea leaves. As we advance in any subject the minutia always ends up being the critical element (see [0]) and so the problem is it doesn't matter if we're 90% "objective" and 10% reading the tea leaves. Not when the decisions are made differentiating the 10%. In reality we're not even good at measuring that 90% when it comes to determining how productive academics are[3-5]
[0] https://www.youtube.com/shorts/JGa_X4QfE-0
[1] https://www.youtube.com/watch?v=EstiCb1gA3U
[2] https://en.wikipedia.org/wiki/List_of_topics_named_after_Leo...
[3] https://briankeating.substack.com/p/peter-higgs-wouldnt-get-...
[4] https://yoshuabengio.org/2020/02/26/time-to-rethink-the-publ...
[5] See the two links in this comment as further evidence. They are about relatively recent Nobel works that faced frequent rejections https://news.ycombinator.com/item?id=47340733
Someone has to pay for all this. That someone is most often not a scientist themselves. They don't have a that vague intuitive research taste that scientists have. Beyond fairly trivial levels of technical correctness, the value of research lies in its narrative implications, its interestingness, its surprise factor etc. These are not objective and are often more about aesthetics and taste than popularly understood. Why does the research matter? To whom does it matter? Are those people important? Do they control resources?
Yes? I even quite explicitly acknowledge that.
There's a cost in either direction. You can't ignore the the costs of reading the tea leaves while acknowledging the costs of unnecessary work. Both have costs.
> But in that same time there was $400bn in waste and fraud through Covid relief funds [1].
The cost of academic fraud should also include the indirect costs of bad decision making.
The Covid relief funds were only needed because politicians implemented extremely aggressive policies based on unproven epidemiological models built on fraudulent practices. I investigated all this extensively at the time and it was really sad/shocking how non-existent intellectual standards are in the field of epidemiology. The models were trash RNGs that couldn't have been validated even if they'd tried, which they never had because the field doesn't consider validation to be necessary to get a paper published. So the models made wildly wrong predictions based on untested, buggy, non-replicable models, which then led to lockdowns, which led to economic catastrophe, which led to the relief programme. All of the fraud in that programme - really the entire cost of it - should be laid at the feet of academic fraud.
This is Goodhart's law at scale. Number of released papers/number of citations is a target. Correctness of those papers/citations is much more difficult so is not being used as a measure.
With that said, due to the apparent sizes of the fraud networks I'm not sure this will be easy to address. Having some kind of kill flag for individuals found to have committed fraud will be needed, but with nation state backing and the size of the groups this may quickly turn into a tit for tat where fraud accusations may not end up being an accurate signal.
May you live in interesting times.
> This is Goodhart's law at scale.
Also, Brandolini's law. And Adam Smith's law of supply and demand. When the ability to produce overwhelms the ability to review or refute, it cheapens the product.
> Number of released papers/number of citations is a target
There was this guy, well connected in the science world, that managed to publish a poor study quite high (PNAS level). It was not fraud, just bad science. There were dozens of papers and letters refuting his claims, highlighting mistakes, and so... Guess what? Attending to metrics (citations, don't matter if they are citing you to say you were wrong and should retract the paper!), the original paper was even more stellar on the eyes of grants and the journal itself.
It was rage bait before Facebook even existed.
There’s an accurate way to confirm fraud: look for inconsistencies and replicate experiments.
If the fraudsters “fail to replicate” legitimate experiments, ask them for details/proof, and replicate the experiment yourself while providing more details/proof. Either they’re running a different experiment, their details have inconsistencies, or they have unreasonable omissions.
Of course this is slightly messy too. Fraudsters are probably always incorrect, of course they could have stolen the data. But being incorrect doesn't mean your intentionally committing fraud.
That would be great if journals bothered publishing replication studies. But since they don't, researchers can't get adequate funding to perform them, and since they can't perform them, they don't exist.
We can't look for failed replication experiments if none exist.
That only confirms a very small subset of fraud. There are many ways to do scientific fraud that will yield internally consistent papers that pass replication as practiced today.
An example is papers which claims of the form, "We proved X by doing Y" where Y is a methodology that isn't derived from and can't prove X. This sort of paper will replicate every time because if you re-derive a correct methodology the original authors say you didn't really replicate their study and your work should be ignored, but if you use their broken methodology you'll just give an intellectually fraudulent paper the stamp of replication approval.
This kind of problem is actually much more widespread than work that looks scientific but in which the data is faked.
that approach is accurate, but not scalable.
the effort to publish a fraudulent study is less (sometimes much less) than the effort to replicate a study.
Is it that easy?
Machine Learning papers, for example, used to have a terrible reputation for being inconsistent and impossible to replicate.
That didn't make them (all) fraudulent, because that requires intent to deceive.
What do you think it is about machine learning that makes it hard to replicate? I'm an outsider to academic research, but it seems like computer based science would be uniquely easy - publish the code, publish the data, and let other people run it. Unless it's a matter of scale, or access to specific hardware.
A lot of things are easy if you ignore the incentive structure. E.g. a lot of papers will no longer be published if the data must be published. You’d lose all published research from ML labs. Many people like you would say “that’s perfectly okay; we don’t need them” but others prefer to be able to see papers like Language Models Are Few-Shot Learners https://arxiv.org/abs/2005.14165
So the answer is that we still want to see a lot of the papers we currently see because knowing the technique helps a lot. So it’s fine to lose replicability here for us. I’d rather have that paper than replicability through dataset openness.
But the lab must publish at least the general category of data, and if that doesn't replicate, then the model only works on a more specific category than they claim (e.g. only their dataset).
Even with the exact same dataset and architecture, ML results aren't perfectly replicable due to random weight initialisation, training data order, and non-deterministic GPU operations. I've trained identical networks on identical data and gotten different final weights and performance metrics.
This doesn't mean the model only works on that specific dataset - it means ML training is inherently stochastic. The question isn't 'can you get identical results' but 'can you get comparable performance on similar data distributions.
Then researchers should re-train their models a couple times, and if they can't get consistent results, figure out why. This doesn't even mean they must throw out the work: a paper "here's why our replications failed" followed by "here's how to eliminate the failure" or "here's why our study is wrong" is useful for future experiments and deserves publication.
As per my previous comment - we are discussing stochastic systems.
By definition, they involve variance that cannot be explained or eliminated through simple repetition. Demanding a 'deterministic' explanation for stochastic noise is a category error; it's like asking a meteorologist to explain why a specific raindrop fell an inch to the left during a storm replication.
Lack of will. That was one of the main results from the survey from Whitaker in 2020. Making your code reusable and easy to understand is significant work that had no direct benefits for a researcher's career. Particularly because research code grows wildly as researchers keep trying thungs.
Working on the next paper is seem as the better choice.
Moreover if your code is easy for others to run then you're likely to be hit with people wanting support, or even open yourself to the risk of someone finding errors in your code (the survey's result, not my own beliefs).
There are other issues, of course. Just running the code doesn't mean something is replicable. Science is replicated when studies are repeated independently by many teams.
There are many other failure modes SOTA-hacking, benchmarking, and lack of rigorous analysis of results, for example. And that's ignoring data leakage or other more silly mistakes (that still happen in published work! In work published in very good venues even)
Authors don't do much of anything to disabuse readers that they didn't simply get really look with their pseudorandom number generators during initialization, shuffling, etc. As long as it beats SOTA who cares if it is actually a meaningful improvement? Of course doing multiple runs with a decent bootstrap to get some estimation of the average behavior os often really expensive and really slow, and deadlines are always so tight. There is also the matter that the field converged on a experimentation methodology that isn't actually correct. Once you start reusing test sets your experiments stop being approximations of a random sampling process and you quickly find yourself outside of the grantees provided by statistical theory (this is a similar sort of mistake as the one scientists in other fields do when interpreting p-values). There be dragons out there and statistical demons might come to eat your heart or your network could converge to an implementation of nethack.
Scale also plays into that, of course, and use of private data as the other comment mentioned.
Ultimately Machine Learning research is just too competitive and moves too fast. There are tens of thousands (hundreds maybe?) of people all working on closely related problems, all rushing to publish their results before someone else published something that overlaps too much with their own work. Nobody is going to be as careful as they should, because they can't afford to. It's more profitable to carefully find the minimal publishable amount of work and do that, splitting a result into several small papers you can pump every few months. The first thing that tends to get sacrificed during that process is reliability.
Yeah, but this happens all the time.
>>95% of the time, the fraudsters get off scot-free. Look at Dan Ariely: Caught red-handed faking data in Excel using the stupidest approach imaginable, and outed as a sex pest in the Epstein files. Duke is still giving him their full backing.
It’s easy to find fraud, but what’s the point if our institutions have rotten all the way through and don’t care, even when there’s a smoking gun?
> Number of released papers/number of citations is a target
Only in stupid university leaderships is that truly what gets you hired or promoted. It's simply not true. Junior researchers in fact are believing it stronger than the facts actually support. Yes, you have to have a solid amount of publications, but doing a ridiculous amount of low-impact salami-sliced stuff or getting your name on a ton of papers where you did no real work is not going to win you a job. People who evaluate applications also live in this world and know that these metrics are being gamed. It's a cat and mouse game but the cats are also paying attention. You can only play this against really dumb government bureaucracies that mechanically give points for publications and have hard numerical criteria etc. Good institutions don't do that.
Good evaluators actually read the papers themselves. Of course you can't read the papers of every single applicant if there are many. But once the applicant gets into the a somewhat filtered down list, reading the paper(s) or having an interview about it, or having them give a talk is much more informative than the number of the papers. Still not perfect, because some people can't communicate well, but communicating is part of the job, so maybe that's super bad but somewhat bad.
Evaluators will use also other evidence such as recommendation letters (informally being aware of the reputation of the recommender), previous fellowships or grants obtained, etc.
None of these are foolproof in themselves. But someone who has super few publications relative to their career stage will need some other piece of evidence in favor.
In machine learning and AI, peer reviews are known to be quite random. If you have a good Arxiv-only paper that makes sense and you can give a good talk on it and answer questions, that will get you further than having a rubberstamp on some paper that's "meh, so what".
There are some players in this game (which includes funding agencies, journals, university administration, hiring committees, conference organizers, students, etc) that are more ossified and slow-moving than others.
And it's also true that double blind peer review and the rubberstamp of a top-tier conference was mostly beneficial to small, not well connected research groups, as it puts the paper on an equal footing with the big labs. The more this system erodes, to more we fall back to reputation and branding of big labs and famous researchers. Again, because there is no infinite time and infinite wisdom available to pick from applicants and there never will be. There are only tradeoffs.
I ran into an interesting incident of this recently. I got a Google Scholar alert about a paper with some experiments related to a paper I had published a while ago, by one "N. Tvlg". I read the paper with interest but I started noticing that although the arguments sounded good, they didn't really make sense, and also the descriptions of the results didn't really match the figures. Eventually I came across a cluster of citations to completely unrelated papers---my field is computational linguistics and these were citations to, like, studies of battery technologies for electric cars. I looked up "N Tvlg" on Google Scholar and they had "published" several articles very recently in totally divergent fields, and upon inspection, all of them had citations back to this materials science research buried deeply somewhere. Clearly these were LLM generated papers trying to build up citation count and h-rank for someone's career.
Where there’s a ranking, there’s someone out there trying to cheat at it. Citation count is a joke.
The purpose of scientific publication used to be to deliver useful scientific results to one's peers. This meant that everyone ran their own personal filter of which peers were working on interesting things, and which collections (journals) were reproducing the most interesting ones. This system still works relatively well for most conscientious researchers. The idea that we should also use publication metrics to rank researchers was never part of this system, and it obviously leads to all sorts of spam (that most scientists just work around) but that seems to really upset non-scientists.
Perhaps relevant to this - if you go to this global ranking of publications:
and select "Mathematics and Computer Science", you'll find the top-ranked university is the University of Electronic Science and Technology of China.My Chinese colleagues have heard of it, but never considered it a top-ranked school, and a quick inspection of their CS faculty pages shows a distinct lack of PhDs from top-ranked Chinese or US schools. It's possible their math faculty is amazing, but I think it's more likely that something underhanded is going on...
It's strange to me that in places full of smart people, it seems to be well understood that this happens and there are lots of anecdotes relating to it; yet the same people will be confused that their political adversaries don't trust "the science" on one issue or another.
Maybe it's the scientists they don't trust?
That’s the beautiful thing about science: You do not have to (and should not) trust any individual. And even if you don’t trust “the consensus” of “the scientific community”, you can empirically verify yourself.
Once you move from abstract to practical - like say having legislators or regulators make rules based on The Science, or relying personally on more facts than you have time to independently verify - yes you do need to have trustworthy people.
Can ordinary civilians feasibly measure, for example, global trends in mean temperature without relying on the data of others?
This is a common misconception in discussions about scientific fraud. You don't have to be able to do a thing correctly to detect when it's being done incorrectly.
You shouldn't trust any claims by scientists about global trends in mean temperatures. We can say this with confidence, without being able to compute a better timeseries, by just looking to see if the basics of the scientific method are being followed by those who do it. If we do that check we find that they don't follow the scientific method. Specifically, they edit past observations to bring them into line with theory instead of deriving theory from data believed to be robust.
https://retractionwatch.com/2021/08/16/will-the-real-hottest...
No, but the literature is open for you to read. Thus you can judge the stated reasoning for yourself. You can also assess how many independent groups are making the same (or closely related) claim.
If only one person claims X then it might be fraud. If large numbers of seemingly unrelated people all claim X then you're forced to decide between X and a global conspiracy to misrepresent X.
To your example. Importantly, even if you deemed one of the global mean temperature datasets to be untrustworthy there are other related (but different) datasets. There are also other pieces of evidence related to the downstream claims that don't look directly at temperature.
Are you going to build a competitor to CERN?
There are many things that cannot be feasibly verified empirically without access to rare resources.
I think it's difficult to relay to the public that a lot of this noise in "scientific publications" is not the same category as real research by reputable institutions. Yes, in certain cases the line can be blurry, fraudsters are sometimes caught in big-name institutions, maybe more in some fields than others, but serious researchers of the field know very well which publication venues and research groups are the real deal and what is bullshit. Overwhelmingly, these fraud papers and nonsense LLM-generated fake stuff are not published in serious journals or conferences.
It's a bit like how can we trust online shopping if I get all these emails trying to sell me aphrodisiac pills?
the "political adversaries" (let's not be coy, you mean conservatives) who "don't trust 'the science'" are less equipped to evaluate said "the science" than the scient_ists_ they don't trust. usually said people were one bad day away from rambling about global jewry and such. due to whatever social illnesses or sociopolitical causes, we've normalized this position, and now measles is back from the dead. but let's pretend that climate change isn't real and the earth's flat and vaccines give you autism, because universities too woke or whatever the fuck
(it's very funny to pretend tech is still, or really much ever was, one big libfest. it's funnier to say this here of all places)
It is useful to distinguish between "effective" scientific fraud, where some set of fraudulent papers are published that drive a discipline in an unproductive direction, and "administrative" scientific fraud, where individuals use pseudo-scientific measures (H-index, rankings, etc) to make allocation decisions (grants, tenure, etc). This article suggests that administrative scientific fraud has become more accessible, but it is very unclear whether this is having a major impact on science as it is practiced.
Non-scientists often seem to think that if a paper is published, it is likely to be true. Most practicing scientists are much more skeptical. When I read a that paper sounds interesting in a high impact journal, I am constantly trying to figure out whether I should believe it. If it goes against a vast amount of science (e.g. bacteria that use arsenic rather than phosphorus in their DNA), I don't believe it (and can think of lots of ways to show that it is wrong). In lower impact journals, papers make claims that are not very surprising, so if they are fraudulent in some way, I don't care.
Science has to be reproducible, but more importantly, it must be possible to build on a set of results to extend them. Some results are hard to reproduce because the methods are technically challenging. But if results cannot be extended, they have little effect. Science really is self-correcting, and correction happens faster for results that matter. Not all fraud has the same impact. Most fraud is unfortunate, and should be reduced, but has a short lived impact.
The distinction between effective and administrative fraud is useful and I think underappreciated. A lot of the conversation in these threads conflates the two, which makes it hard to reason about what actually needs fixing.
I want to push back a little on "science is self-correcting" though. It's true in the limit, but correction has a latency, and that latency has real costs. In fields like nutrition, psychology, or pharmacology, a fraudulent or deeply flawed result can shape clinical guidelines, public policy, and drug development pipelines for a decade or more before the correction lands. The people harmed during that window don't get made whole by the eventual retraction.
The comparison I keep coming back to is fault tolerance in distributed systems. You can build a system that's "eventually consistent" and still have it be practically broken if convergence takes too long or if bad state propagates faster than corrections do. The fraud networks described in TFA are basically an adversarial workload against a system (peer review) that was designed for a much lower rate of bad input. Saying the system self-corrects is accurate, but it's not the same as saying the system is healthy or that the current correction rate is adequate.
I think the practical question isn't whether science corrects itself in theory but whether the feedback loops are fast enough relative to the rate of fraud production, and right now the answer seems pretty clearly no.
Re self-correcting science. In the area I am most familiar with (basic life sciences), correction happens pretty quickly. But I don’t worry about public policy much.
But I’m comfortable arguing that where science intersects with policy, fraud plays a very minor role. I suspect that most policy “mistakes” (policies that were adopted and then reversed) are more about the need for a policy in the absence of data (covid and masks), or subtle tradeoffs (covid and masks), or a policy choice that seems slightly better than an alternative (mammography) but also has poorly understood harms. Policy involves politics, and science unfortunately plays less of a role than one might like (and fraudulent science an even smaller role). This is not my field, but I cannot think of policies that were reversed because of discoveries of fraud (perhaps thalidomide and other drug approvals).
>methods are technically challenging.
And finanacially too..
>Science really is self-correcting..
When economy allows it....
My wife completed her PhD two years ago and she put a LOT of work into it. Many sleepless nights, and it almost destroyed our marriage. It took her about 6 years of non-stop madness and she didn’t even work during that time. She said that many of her colleagues engaged in fraudulent data generation and sometimes just complete forgery of anything and everything. It was obvious some people were barely capable of putting together coherent sentences in posts, but somehow they generated a perfect dissertation in the end. It was common knowledge that candidates often hired writers and even experts like statisticians to do most of the heavy lifting. I don’t know if this is the norm now, but I simultaneously have more respect and less respect for those doctoral degrees, knowing that some poured their heart and soul into it, while others essentially cheated their way through. OTOH, I also understand that there may be a lot of grey area.
My eyes have been opened!
I found the article and your third-hand anecdotes troubling. The good news is that it does not match any of the years of experience in my field. Fraud is just not that rampant. At PhD-granting institutions, the level of fraud you describe here is very seriously punished. It's career-ending. The violations that you are serious enough that any institution would expel said students (or harshly punish faculty--probably firing them). She did no one any favors by not reporting them.
Unfortunately I don't think a dialogue around vague anecdotes is going to be particularly enlightening. What matters is culture, but also process--mechanisms and checks--plus consequences. Consequences don't happen if everyone is hush-hush about it and no one wants to be a "rat".
>It's career-ending..
That is where being good at politics come into play. And if you are good at it, instead of being career-ending, fraud will put you in the highest of the positions!
No one wants a "plant" who cannot navigate scrutiny!
> The good news is that it does not match any of the years of experience in my field.
I worked for exactly one academic, and he indulged in impossible-to-detect research fraud. So in my own limited experience research fraud was 100%.
It was a biology lab, and this was an extremely hard working man. 18 hours per day in the lab was the norm. But the data wasn't coming out the way he wanted, and his career was at stake, so he put his thumb on the scale in various ways to get the data he needed. E.g. he didn't like one neural recording, so he repeated it until he got what he wanted and ignored the others. You would have to be right in the middle of the experiment to notice anything, and he just waved me off when I did.
This same professor was the loudest voice in the department when it came to critiquing experimental designs and championing rigor. I knew what he did was wrong, because he taught me that. And he really appeared to mean it, but when push came to shove, he fiddled, and was probably even lying to himself.
So I came away feeling that academic fraud is probably rampant, because the incentives all align that way. Anyone with the extraordinary integrity to resist was generally self-curated out of the job.
I had a somewhat similar experience- was a postdoc for a pre-tenure professor at berkeley. after writing up a paper based on her methods, with poor results, I handed the draft to her. She rewrote it- basically adding carefully worded/presented results that made it look as good as possible. And then submitted it (to a niche conference where the editor was a buddy of hers). When I read her submission I asked her to remove my name from it and she immediately withdrew the submission. I left her lab shortly after because I am not going to tarnish my publication record with iffy papers like that.
Over time I learned that most papers in my field (computational biology) are embellished to some extent or another (or cherry-picked/curated/structured for success) and often irreproducible- some key step is left out, or no code is provided that replicates the results, etc. I can see this from two perspectives:
1) science should be trivially reproducible; it should not require the smartest/most capable people in the field to read the paper and reproduce the results. This places a burden on the people who are at the state of the art of the field to make it easy for other folks, which slows them down (but presumably makes overall progress go faster).
2) science should be done by geniuses; the leaders in the field don't need to replicate their competitors paper. it's sufficient to read the paper, apply priors, and move on (possibly learning whatever novel method/technique the paper shows so they can apply it in their own hands). It allows the field innovators to move quickly and discover new things, but is prone to all sorts of reliability/reproducibility problems, and ideally science should be egalitarian, not credentials-based.
My cousin (with whom I am very close) had a similar experience that I posted about years ago. https://news.ycombinator.com/item?id=32969092
I have repeated it many times on this site but here’s the reality of human experience: if the rate of fraudulent labs is even as high as 10% you should expect that any viewpoint that it’s widespread would be drowned out by views that it’s not real.
Also, the phenomenon you observed where people are champions till the rubber meets the road is more common than one thinks.
> if the rate of fraudulent labs is even as high as 10% you should expect that any viewpoint that it’s widespread would be drowned out by views that it’s not real.
If "it" is fraud here I would expect the viewpoint that it's widespread to be less and less drowned out as it approached 10% since everyone would know that it's real. I think I'm misunderstanding the sentence.
No, the guys at fraudulent labs and the guys at honest labs will both claim no fraud. The only ones who will claim fraud are those who cross over. So you’ll get a vast majority telling you it’s not happening and a tiny minority (even when as high as 10% are fraud) telling you the fact. All rare things have this effect. There will be so many people telling you it’s not real “as someone in the field”. They will be adamant about it. You need someone who has seen both.
To be clear, not “as it approaches 10%”. I mean “even as high as 10%”.
Not I didn't say that it isn't real, I just said it's not that rampant, and I was referring to the egregious examples in the article and the anecdote. But it's a sliding scale and being selective in presenting research results is a gray area that requires us to think about reproducibility and make sure we have good processes for discouraging, detecting, and correcting those kinds of dishonesty, too.
In my field (programming languages and compilers), conferences have started to adopt artifact submissions which include source and build instructions that are then independently replicated by volunteers who serve on artifact evaluation committees. It's a good step in the right direction.
And I'll also caution, again, that reasoning based on anecdotes can be distorted both ways. A tiny minority of people can claim the sky is falling and make it sound like Science is threatened by crisis. Those voices get amplified by the media and the attention economy and get blown out of proportion. What Science and institutions need is mechanisms that are fair and well-resourced and taken seriously. Those mechanisms and the people who volunteer time and effort to keep them running are the real heroes of Science, and breathless articles and anonymous anecdotes on the internet do not make up an accountability system.
If you see fraud, then REPORT IT to someone who can actually hold them accountable. Unsubstantiated and undirected accusations just spread distrust and do not increase accountability.
>And I'll also caution, again, that reasoning based on anecdotes can be distorted both ways...
I will go further and say that if you can clearly see how the incentives are aligned, even anecdotes are not necessary.
>If you see fraud, then REPORT IT to someone who can actually hold them accountable. Unsubstantiated and undirected accusations just spread distrust and do not increase accountability.
Mmmm..are you..for real? I mentioned incentives just above, so let us look at it. What is the incentive of this "person who can hold them accountable"? Do they really want to cut down the "output" of the institution by insisting on 100% honest? Do they really want to lose to competition which might allow such practices?
I think not. I think if this is reported, chances that the report will sit in some shelves or end up in some dust bin doing nothing. And the person who report it would suffer one way or the other.
Also: https://gwern.net/doc/sociology/2003-ashforth.pdf
>What Science and institutions need is mechanisms...
Na, "Science" and "Scientists" needs funding. End of story.
What field? I am aware this kind of stuff happens, but I don't really see it among any of my colleagues.
yeah - skeptical here. Among certain departments, at large schools, under certain leaders.. The combination of "my marriage almost crumbled" for motivated reasoning, and "I have never seen any of this before" total inexperience with actual process.. the post shows itself to be biased and unreliable.
However, among certain departments, at large schools, under certain leaders.. yes, and growing
$0.02
Fucking hilarious to me when people claim academics are motivated by the "money", eg, when claimed by climate deniers.
That's approximately 1 million people. Even a religious cult that size would have difficulty controlling motivations. As an example:
> Petitioners also formed a variety of organizations to create what they termed "marketable science." Pet. App. 1687a. For example, through the Council for To bacco Research (CTR) and Lawyers' Special Accounts, petitioners jointly financed research programs that were directed by company lawyers and calculated to yield favorable results. Id. at 240a-275a. Petitioners regu larly cited the conclusions of the scientists funded through these programs as if they were the objective results of disinterested research, without revealing that the scientists had, in fact, been funded by the industry. Id. at 195a.
That comes from here: https://www.justice.gov/osg/brief/philip-morris-usa-inc-v-un...
It's possible all the science was good but people were upset about who funded it.
Undoubtably climate science is the exception and immune from fraudulent data generation and sometimes complete forgery
I'm sure there's some, but the small point here is that it almost certainly is more motivated by factors other than financial gain. I'm sure it you search you can find such cases though.
The much broader point though is the dismissal of the bulk consensus of academic research because academics are in it for the "money".
This is what happens when people argue past each other on "Trust the science".
Science is good, but it's mediated via corruptible humans.
Also, "science" isn't some sort of dogma that you should trust, it's a process you should follow.
"Trust the science" is anathema to the process. If anything, the chant should be "Doubt the science! Give it your best shot, refute it with data, with logic, provide a better explanation!"
Realistically, the vast majority of people will not have a real chance to "refute" or even evaluate scientific claims. Maybe given a lot of time and foundational work to learn the field, some percentage of people can usefully think about them, but the vast majority can't. A lot of people are functional illiterates. They will pick based on trust and gut feelings either way.
For example, when deciding whether to give your kids certain vaccines or not, you really can't expect that new parents will read the primary literature and try to refute or confirm the conclusions based on the numbers and will trace through the citations and so on... Any of those claims will also have some online account on social media refuting it with equally scientifically sounding words. In the end it will come down to heuristics and your model of how the world works, which set of people operate with what kind of intention. Like maybe you know people working in the field who you trust and hear from them that generally this sort of stuff can be trusted. Or maybe you had some bad experiences getting screwed by "the establishment" (maybe even unrelated to medicine) and now you lump all this together and distrust them.
Which is why we need people "doing science" to also focus on getting rid of bad ideas rather than just coming up with more. The present incentive structure is such that we reward people for coming up with shocking new ideas even if they are obviously rubbish and don't do enough to reward the ones who put in the effort to debunking existing bad ideas.
Coming up with ideas is the easy part of science, but most new ideas are wrong. Getting rid of the ones that aren't actually correct is hard, yet we shower praise on people doing the easy part and ignore the ones doing the hard part.
It always comes back to Goodhart's Law and our apparent inability to create sustainable incentive structures.
More broadly, an incredible amount of our society's systems are built around actors being uncoordinated. Redesigning institutions to resist networks of coordinated action between seemingly unlinked individuals will, in my opinion, be one of the great social challenges of this era.
This is the part that feels hardest to fix: once a system starts rewarding throughput over scrutiny, fraud stops looking like individual misconduct and starts looking like a supply chain problem.
One approach is more integration of researchers with businesses. Fraud (or simple incompetence) by researchers negatively affects businesses, as they expend effort on things that aren't real. I understand this is a constant problem in the pharmaceutical industry.
It's quite possible to be very successful marketing and selling things that aren't real. The market consists of humans, not perfectly rational machines.
Even so, businesses largely compete based on whether their products are worth buying. That means bad research is bad for business.
the problem is two-fold in my opinion.
firstly, there are basically no legal repercussions for scientific misconduct (e.g. falsifying data, fake images, etc.). most individuals who are caught doing this get either 1) a slap on the wrist if they are too big to fail or in the employ of those who are too big to fail or 2) disbarred, banned, and lose their jobs. i don't see why you can go to jail for lying to investors about the number of users in your app but don't go to jail for lying to the public, government, and members of the scientific community about your results.
secondly, due to the over production of PhD's and limited number of professorship slots competition has become so incredibly intense that in order to even be considered for these jobs you must have Nature, Cell, and Science papers (or the field equivalent). for those desperate for the job their academic career is over either way if they caught falsifying data or if they don't get the professorship. so if your project is not going the way you want it to then...
sad state of things all around. i've personally witnessed enough misconduct that i have made the decision to leave the field entirely and go do something else.
I unironically agree, p-hacking should be a criminal offense.
If you get paid by the government to do research you should make all your raw data, code, results etc, accessible to the public.
If it then turns out any of it is fabricated, you should be personally liable for paying it back
why would anyone actually interested in scientific research come to this, since it literally undermines the whole practice of science?
Publish or perish. Academia requiring PhDs to publish or be fired. It's made entire fields echo chambers and prone to political influence.
so a perverse incentive, basically. shocker. got it
There has always been a lot of bad science. I would suggest that percentage has only marginally increased.
Are these "entities" named and shamed somewhere? I just scanned the paper but couldn't find explicit mentions.
The future of science, the Internet, and all things: The Library of Babel by Jorge Luis Borges.
Some things should not have been democratized. Silicon Valley assumes that removing restrictions on information brings freedom, but reality shows that was naïve.
You shouldn't just assume that the inverse would be free from fraud. The incentives for fraud still apply even when the system is not democratized.
Except with AI, a fraudulent gatekept world would still be a smaller percentage of fraud than what is coming. Infinite scale fraud.
The soviets may have rigged a few studies; but the democratized world now faces almost all studies being rigged.
I think it'd be a different form of fraud that would be much harder to discredit. Think sugar industry blaming fat for health issues. More of that.
The Library of Babel comparison is too fatalistic imo, even granting that it's maybe just an extreme example. The real world doesn't quite resemble a closed system with no metadata. We can still establish chains of trust.
Whether or not people will build resilient chains is another story, contingent on whether the strength of that chain actually matters to people. It probably doesn't for a lot of people. Boo. But inasmuch as I care, I feel I ought to be free to try and derive a strong signal through the noise.
In what way was it was democratised? We're not talking about Substacks and YouTube channels here, we're not even talking about arXiv preprints and the like, we're talking about peer-reviewed journal publications, and that system remains gated in much the same way that it was in the 1980s when it comes to trying to publish in it. If anything this system is the poster child for top-down gatekeeping by the recognised authorities, and it's precisely the value of that official recognition that makes people so desperate to break into it. The major changes seem to have been the easy availability of author publication lists and the advent of publication metrics, not things which have been or were ever meant to be particularly democratising for would-be authors; and an increase in the number of people playing the game, driven to a large extent by increasing participation from developing countries, and hopefully not many people would have the gall to argue for a ban on developing-country participation.
Tearing down gatekeeping (i.e. "high standards") in pursuit of maximal inclusivity is just another way of saying "regression to the mean."
The gate has been removed from the signal chain, and now the noise floor is at infinity.
there is no 'sin of maximal inclusivity here', the gate is broken, but primarily because it was largely an honor system before, and no one has the motivation or resources to really dig into a lot of these papers.
in no sense was it corrupted by the desire to include a larger population in journal publications.
There is a saying in my native language that goes something like "If you mix poison and milk, the milk will turn poisonous, instead of poison becoming milk (aka beneficial)".
I guess, to convert it into this context, we can say that if you mix the high minded and infantile (which I think is what Internet and social media did), the high minded becomes infantile, instead of the other way around.
Almost as if capitalism makes everything into a market, and the profits make it self sustaining.
How many will see the connections between this and our capitalist mode of production? Probably few since modern lit/news is allergic to systemic analysis.
The blatant flaws of capitalism can't be ignored for much longer.
All people in my extended family were Soviet scientists and engineers from multiple fields, and outside of experimental physics it was the same or worse. Same publish or perish pressure, same amount of fraud and lack of reproducibility. A ton of papers were made up. My father's lab lead was an absolute fraud (biochemistry), everybody knew that, and my father was unable to speak up until the late 90's.
When I was a kid I thought it was the issue with USSR rotting to the core (it was), but when it crashed and later when the web appeared, it became obvious that it's a common problem with academia and its incentives.
The only way to curb something like that is more democratization. Fraud is a common problem in any system anywhere. A reputation score on top would help, only if it can be kept democratically controlled.
There is no single solution, but public fund usurping is basically a law of capitalism, which is why I critique it in this context. Public money laundering is a developed industry in capitalism.
What I get from this is that the professional academic community -- as a whole -- has hit critical mass, which has produced a cottage industry of paper mills and fraudulent services to support said surplus.
Socialism wouldn't be the answer to this because socialism is famous for struggling with surpluses and shortages. All socialism would do is clamp down (hard) on academic's, which case you wind up with the famous shortage where not enough PHD's are available to produce research for an industry.
And that's not a problem specific to just socialism, that's the fallacy of central-planning. The US government clamped down on welfare fraud and the result were freak government social workers sniffing people's bed sheets and rooting through drawers and forcing everyone to document partners.
This is the situation where there needs to be a market correction because the alternative could be far worse.
It's the tax-payer funded business model, the NGO trap. Subsidies, grants, tax-breaks, credit, deductions, exemptions, etc. A whole class of profiteers live in this sector. Even though academia funding isn't strictly categorized as an NGO, it still fits/foots the bill. Public funding of private gains is the oldest trick in the book. Ask any capitalist, they know. And I'm not saying I'm against public funding, but this is often codified into a mafia of sorts when enough money flows through.
The real problem here is the fundamental lack of democratic control over our agencies. That our political organization is intensely lagging behind our productive organization. That our whole political will involves TRUSTING strangers to not be corrupt instead of directly democratizing these processes as much as possible.
But besides that, you cannot remove history from historical analysis. The reason socialism countries struggled in the beginning wasn't an inherent flaw in its organization, but the fact that they were under constant war war by capitalist countries through out their existence. Also keep in mind that most socialist countries did NOT have a whole section of the world where-from to extract riches through murder (S.America, Africa, Middle east, etc), like western capitalist countries had. This is convenient for you to ignore. Maybe because you don't know, or don't care about the super-exploitative history of these places and how they tie into western capitalism. But they are inherent to western wealth and these countries' whole history is struggle against this exploitation.
Not to mention that most of the countries on earth are capitalists and are very very very poor.
To add: Socialism has nothing to do with "clamping down" on X or Y industry, as you hypothetically claim would happen. Socialism is almost exclusively about removing the need to generate capital from production. It unleashes production from its historical ball and chain that is profiteering.
In a single sentence: Instead of production being held back by capitalists generating wealth we can produce for our own needs. It is self sustaining production.
Central planning is not fallacious. Your problem is with corruption, not democratic central planning. The US Govt is a pro-capitalist entity that pro-capitalists try to distance themselves from (ironically). So using them as an example isn't saying anything at all.
Central planning is not "allow a small group of people to decide things", as happens in the US Govt. Central planning is to take into account all sources of information on production to plan said production democratically.
This will always beat the highly highly inefficient speculation of capitalism. Where trillions vanish on a whim and cause of a tweet, where crisis occur every 8-10 years, and where its whole trade market is built to hide that it is mostly insider trading. Again, your problem is with corruption not democratic central planning.
And the way to deal with corruption is to create more democratic bodies where avg people hold real power. I don't see you asking for that either. We call that socialism.
Capitalism certainly is hugely flawed and yet it is far less flawed than any other economic system we know of. Experimentation with the foundations of society is about as risky as it gets. You could end up with a utopia or you could end up with another USSR. History tells us which outcome is more likely
People say that because they are not aware of what Marxism is.
Marxism isnt "lets try something different based on ideas of justice"
Marxism is "Society evolves through general stages: primitive, slave, feudal and capitalist, this is determined by the level production. Capitalism is holding back its current evolution into a society of plenty."
Good luck backing speculation and profit gatekept production.
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Industry >> Academia
Profits are the deciding factor, not honor.