"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. ..."
A crazy world we live in where Robert Maxwell's daughter is more notorious than he is.
Shit apple doesn’t fall far from the shit tree I guess.
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.
perhaps a bit off-topic, but what is coincidental about this and/or what is the relevance of Ghislaine Maxwell here?
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.
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
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.
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.
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.
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.
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.
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.
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.
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?
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.
> Sounds like politics in a nutshell.
Again, no. It sounds like the division of labor. The thing that made modern human societies possible.
The jokes write themselves,
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 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.
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.
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.
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.
More than anything. That might legitimately be enough to save science on its own.
https://blog.plan99.net/replication-studies-cant-fix-science...
(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)
But they don't, and that's the problem!
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.)
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...
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.
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.
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...
As for your point, I don't really understand what you are trying to say.
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
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...
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.
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.
> '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
>>> Advanced groups usually replicate their competitor's results in their own hands shortly after publication
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.
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).
> physics tends to be much more reproducible than biology, and CS doubly-so.
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.
[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).
Actually, yes, I do. 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.
All because journals prefer novelty over confirmation. It's like a castle of cards, looks cool but not stable or long-term at all.
> 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'm sure you can more narrowly tune your email alerts FFS.
Hell yeah. We’re all trying to get that Nature paper. Imagine if you could accomplish that by setting the record straight.
I believe people will enthusiastically say yes but that they do not routinely read that journal.
"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.
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.)
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.
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.
Oh there have been times would have loved to be able to apply for one of those!
This is partly why much of today's science is bs, pure and simple.
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
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?
The simple fact that theories should be falsified and not verified is something that most scientists don’t know.
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.
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.
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.
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.
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.
"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.
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.
> 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] (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...
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.
> 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
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.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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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...
Maybe it's the scientists they don't trust?
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...
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.
There are many things that cannot be feasibly verified empirically without access to rare resources.
It's a bit like how can we trust online shopping if I get all these emails trying to sell me aphrodisiac pills?
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.
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.
And finanacially too..
>Science really is self-correcting..
When economy allows it....
My eyes have been opened!
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!
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.
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 "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.
To be clear, not “as it approaches 10%”. I mean “even as high as 10%”.
However, among certain departments, at large schools, under certain leaders.. yes, and growing
$0.02
The much broader point though is the dismissal of the bulk consensus of academic research because academics are in it for the "money".
> 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.
Science is good, but it's mediated via corruptible humans.
"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!"
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.
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.
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.
If it then turns out any of it is fabricated, you should be personally liable for paying it back
Some things should not have been democratized. Silicon Valley assumes that removing restrictions on information brings freedom, but reality shows that was naïve.
The soviets may have rigged a few studies; but the democratized world now faces almost all studies being rigged.
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.
The gate has been removed from the signal chain, and now the noise floor is at infinity.
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.
in no sense was it corrupted by the desire to include a larger population in journal publications.
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.
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.
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.
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.
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.
Profits are the deciding factor, not honor.