This is Goodhart's law at scale. Number of released papers/number of citations is a target. Correctness of those papers/citations is much more difficult so is not being used as a measure.

With that said, due to the apparent sizes of the fraud networks I'm not sure this will be easy to address. Having some kind of kill flag for individuals found to have committed fraud will be needed, but with nation state backing and the size of the groups this may quickly turn into a tit for tat where fraud accusations may not end up being an accurate signal.

May you live in interesting times.

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bwfan123 6 hours ago | parent | next [-]

> This is Goodhart's law at scale.

Also, Brandolini's law. And Adam Smith's law of supply and demand. When the ability to produce overwhelms the ability to review or refute, it cheapens the product.

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bonoboTP an hour ago | parent | prev | next [-]

> Number of released papers/number of citations is a target

Only in stupid university leaderships is that truly what gets you hired or promoted. It's simply not true. Junior researchers in fact are believing it stronger than the facts actually support. Yes, you have to have a solid amount of publications, but doing a ridiculous amount of low-impact salami-sliced stuff or getting your name on a ton of papers where you did no real work is not going to win you a job. People who evaluate applications also live in this world and know that these metrics are being gamed. It's a cat and mouse game but the cats are also paying attention. You can only play this against really dumb government bureaucracies that mechanically give points for publications and have hard numerical criteria etc. Good institutions don't do that.

	▲	33 minutes ago \| parent [-]
		[deleted]

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otherme123 5 hours ago | parent | prev | next [-]

> Number of released papers/number of citations is a target

There was this guy, well connected in the science world, that managed to publish a poor study quite high (PNAS level). It was not fraud, just bad science. There were dozens of papers and letters refuting his claims, highlighting mistakes, and so... Guess what? Attending to metrics (citations, don't matter if they are citing you to say you were wrong and should retract the paper!), the original paper was even more stellar on the eyes of grants and the journal itself.

It was rage bait before Facebook even existed.

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armchairhacker 7 hours ago | parent | prev [-]

There’s an accurate way to confirm fraud: look for inconsistencies and replicate experiments.

If the fraudsters “fail to replicate” legitimate experiments, ask them for details/proof, and replicate the experiment yourself while providing more details/proof. Either they’re running a different experiment, their details have inconsistencies, or they have unreasonable omissions.

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pixl97 7 hours ago | parent | next [-]

Of course this is slightly messy too. Fraudsters are probably always incorrect, of course they could have stolen the data. But being incorrect doesn't mean your intentionally committing fraud.

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ertgbnm 4 hours ago | parent | prev | next [-]

That would be great if journals bothered publishing replication studies. But since they don't, researchers can't get adequate funding to perform them, and since they can't perform them, they don't exist.

We can't look for failed replication experiments if none exist.

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john_strinlai 6 hours ago | parent | prev | next [-]

that approach is accurate, but not scalable.

the effort to publish a fraudulent study is less (sometimes much less) than the effort to replicate a study.

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wswope 7 hours ago | parent | prev | next [-]

Yeah, but this happens all the time.

>>95% of the time, the fraudsters get off scot-free. Look at Dan Ariely: Caught red-handed faking data in Excel using the stupidest approach imaginable, and outed as a sex pest in the Epstein files. Duke is still giving him their full backing.

It’s easy to find fraud, but what’s the point if our institutions have rotten all the way through and don’t care, even when there’s a smoking gun?

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awesome_dude 6 hours ago | parent | prev [-]

Is it that easy?

Machine Learning papers, for example, used to have a terrible reputation for being inconsistent and impossible to replicate.

That didn't make them (all) fraudulent, because that requires intent to deceive.

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itintheory 5 hours ago | parent [-]

What do you think it is about machine learning that makes it hard to replicate? I'm an outsider to academic research, but it seems like computer based science would be uniquely easy - publish the code, publish the data, and let other people run it. Unless it's a matter of scale, or access to specific hardware.

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renewiltord 5 hours ago | parent [-]

A lot of things are easy if you ignore the incentive structure. E.g. a lot of papers will no longer be published if the data must be published. You’d lose all published research from ML labs. Many people like you would say “that’s perfectly okay; we don’t need them” but others prefer to be able to see papers like Language Models Are Few-Shot Learners https://arxiv.org/abs/2005.14165

So the answer is that we still want to see a lot of the papers we currently see because knowing the technique helps a lot. So it’s fine to lose replicability here for us. I’d rather have that paper than replicability through dataset openness.

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armchairhacker 4 hours ago | parent [-]

But the lab must publish at least the general category of data, and if that doesn't replicate, then the model only works on a more specific category than they claim (e.g. only their dataset).

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awesome_dude 3 hours ago | parent [-]

Even with the exact same dataset and architecture, ML results aren't perfectly replicable due to random weight initialisation, training data order, and non-deterministic GPU operations. I've trained identical networks on identical data and gotten different final weights and performance metrics.

This doesn't mean the model only works on that specific dataset - it means ML training is inherently stochastic. The question isn't 'can you get identical results' but 'can you get comparable performance on similar data distributions.

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armchairhacker 3 hours ago | parent [-]

Then researchers should re-train their models a couple times, and if they can't get consistent results, figure out why. This doesn't even mean they must throw out the work: a paper "here's why our replications failed" followed by "here's how to eliminate the failure" or "here's why our study is wrong" is useful for future experiments and deserves publication.

	▲	awesome_dude 2 hours ago \| parent [-]
		As per my previous comment - we are discussing stochastic systems. By definition, they involve variance that cannot be explained or eliminated through simple repetition. Demanding a 'deterministic' explanation for stochastic noise is a category error; it's like asking a meteorologist to explain why a specific raindrop fell an inch to the left during a storm replication.