Some things should not have been democratized. Silicon Valley assumes that removing restrictions on information brings freedom, but reality shows that was naïve.
https://traditional.leidenranking.com/ranking/2025/list
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...
Profits are the deciding factor, not honor.
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.
Maybe it's the scientists they don't trust?
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.
Science is good, but it's mediated via corruptible humans.
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.
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.
My eyes have been opened!
If it then turns out any of it is fabricated, you should be personally liable for paying it back
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.
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.
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.
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.
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.
> Most will refuse to publish replications, negative studies, or anything they deem unimportant, even if the study was conducted correctly.
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]. 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...
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.
"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!"
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.
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. When you demand results from a system held together by honesty and good faith, it can't continue for long.
And finanacially too..
>Science really is self-correcting..
When economy allows it....
The gate has been removed from the signal chain, and now the noise floor is at infinity.
The soviets may have rigged a few studies; but the democratized world now faces almost all studies being rigged.
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".
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.
However, among certain departments, at large schools, under certain leaders.. yes, and growing
$0.02
We can't look for failed replication experiments if none exist.
the effort to publish a fraudulent study is less (sometimes much less) than the effort to replicate a study.
>>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?
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.
A crazy world we live in where Robert Maxwell's daughter is more notorious than he is.
I believe people will enthusiastically say yes but that they do not routinely read that journal.
This is partly why much of today's science is bs, pure and simple.
in no sense was it corrupted by the desire to include a larger population in journal publications.
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!
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.
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.
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.
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.
There are many things that cannot be feasibly verified empirically without access to rare resources.
Shit apple doesn’t fall far from the shit tree I guess.
All because journals prefer novelty over confirmation. It's like a castle of cards, looks cool but not stable or long-term at all.
I'm sure you can more narrowly tune your email alerts FFS.
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.
perhaps a bit off-topic, but what is coincidental about this and/or what is the relevance of Ghislaine Maxwell here?
Actually, yes, I do. The marginal cost for publishing a study online at this point is essentially nil.
Hell yeah. We’re all trying to get that Nature paper. Imagine if you could accomplish that by setting the record straight.
More than anything. That might legitimately be enough to save science on its own.
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.
> 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?
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.
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.
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.
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.)
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.
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.
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.
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.
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.
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
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.
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.
(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)
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.
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.
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?
"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.
But they don't, and that's the problem!
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.
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 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.
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.
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.
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!
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.
> Sounds like politics in a nutshell.
Again, no. It sounds like the division of labor. The thing that made modern human societies possible.
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.