Entities enabling scientific fraud at scale (2025) (doi.org)

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 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. ..."

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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.

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.

It's useless, but I'm ashamed to admit I found this tiny piece of trivia interesting.

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

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.

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.

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.

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.

Replication studies cannot save science and might make the fraud problem worse.

https://blog.plan99.net/replication-studies-cant-fix-science...

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)

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.

Why blame just the journals when every other system also disintivizes the same.

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.

Suggesting that people would stop reading Nature if they also included replication studies send like an incredible leap.

Tagging seems like an option here.

> 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?

paid by the original authors if their study fails to replicate

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.

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.

  > 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

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

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.

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.

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.

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".

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.

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.