[Renevith]

Elisabeth Bik is a real hero. the amount of unpaid hours she has put into detecting fraud in published scientific papers is unbelievable and a true service to the scientific community.

Her work is pure positive externality: nobody has incentive to pay for it. Journals barely care about fraud (as long as it's not caught by someone else), reviewers don't have much incentive to check carefully for it (and might even be in on the fraud), and the authors engaging in fraud obviously prefer that journals not look too closely.

That's why I am a patron: https://www.patreon.com/elisabethbik

She maintains a blog if you want to read more about what she does, e.g.: https://scienceintegritydigest.com/2020/12/31/2020-a-year-in...

[kens]

Elisabeth Bik is worth following on Twitter: https://twitter.com/MicrobiomDigest She posts images from widely-cited scientific papers and then her followers race to find the duplications in the images. These challenges range from easy to very hard.

[jdale27]

Her work is pure positive externality: nobody has incentive to pay for it. Journals barely care about fraud (as long as it's not caught by someone else), reviewers don't have much incentive to check carefully for it (and might even be in on the fraud), and the authors engaging in fraud obviously prefer that journals not look too closely.

There are many more interested parties than journals, reviewers, and authors. Universities have incentives to protect their reputations from fraudulent scientists; funding agencies have incentives to protect their funds from being misused, etc. Certainly they don't have resources to exhaustively check the outputs of their scientists, however, and it seems difficult or impossible to quantify the return on investment of such proactive research integrity efforts.

This seems like an area where one of the large science-focused philanthropic organizations -- the Gates Foundation, Allen Institute, Chan Zuckerberg, HHMI, etc. -- would be well positioned to have a huge impact on the quality of scientific output and also reap a large return in terms of positive press for their efforts. Any of these organizations could easily fund an entire team of Elisabeth Biks, not to mention an AI team on the side to augment and partially automate this work.

[jryb]

The problem is really that there are strong incentives to produce this stuff, usually from the universities or hospitals themselves. These aren’t prestigious places, and it’s not in their interest to spotlight how many garbage papers they produce.

[orzig]

Thanks for letting me/us know about the Patreon! I'm now a happy contributor.

[lixtra]

She advocates to „Limit spread of [scientific] misinformation on social media“ [1] yet uses social media to attack published and peer reviewed science (the gold standard used by fact checkers).

[1] https://mobile.twitter.com/MicrobiomDigest/status/1410653746...

[NtochkaNzvanova]

She calls attention to instances of fraud that have made it through peer review and publication. Are you saying that is somehow comparable to spreading "misinformation" on social media?

Published research is not perfect; tons of crap gets through peer review. Every scientist knows this. Peer reviewed science may be more reliable than other forms of information, but treating it as settled and unquestionable is foolish and, in fact, unscientific.

[lixtra]

My problem is her call for censorship of misinformation.

Currently institutional judgment on misinformation relies on fact checkers, that in best case turn to published research, which is not perfect as you pointed out.

Depending on the zeitgeist in the future she calls for, she may be censored herself.

[FooHentai]

An expansion of that point in your original message might have been better received. The way it was phrased read like you yourself held the opinion that she was using social media to spread lies.

[anitil]

That tweet you linked was talking about her ideas about how to improve scientific integrity. I would hardly characterize it as a call to censorship.

[bordercases]

As a "gold-standard" it's still fallible. Peer-reviewed studies can and do fail replication.