It's worth pointing out that most of the best science happened before peer review was dominant.
There's an article I came across awhile back, that I can't easily find now, that basically mapped out the history of our current peer review system. Peer review as we know it today was largely born in the 70s and a response to several funding crises in academia. Peer review was a strategy to make research appear more credible.
The most damning critique of peer-review of course is that it completely failed to stop (and arguably aided) the reproducibility crisis. We have an academic system where the prime motivation is the secure funding through the image of credibility, which from first principles is a recipe for wide spread fraud.
>It's worth pointing out that most of the best science happened before peer review was dominant.
It's worth pointing out that most of everything happened before peer review was dominant. Given how many advances we've made in the past 50 years, so I'm not super sure everyone would agree with your statement. If they did, they'd probably also agree that most of the worst science also happened before peer review was dominant, too, though.
Our advances in the last 50 years have largely been in engineering, not science. You could probably take a random physics professor from 1970 and they'd not sweat too much trying to teach physics at the graduate level today.
But a biology professor from that time period would have a lot of catching up to do, perhaps too much, especially (but not only) if any part of their work touched molecular biology or genetics.
But there is zero reason why the definition of peer review hasn't immediately been extended to include:
- accessing and verifying the datasets (in some tamper-proof mechanism that has an audit trail). Ditto the code. This would have detected the Francesca Gino and Dan Ariely alleged frauds, and many others. It's much easier in domains like behavioral psychology where the dataset size is spreadsheets << 1Mb instead of Gb or Tb.
- picking a selective sample of papers to check reproducibility on; you can't verify all submissions, but you sure could verify most accepted papers, also the top-1000 most cited new papers each year in each field, etc. This would prevent the worst excesses.
PS a superb overview video [0] by Pete Judo "6 Ways Scientists Fake Their Data" (p-hacking, data peeking, variable manipulation, hypothesis-shopping and selectively choosing the sample, selective reporting, also questionable outlier treatment). Based on article [1]. Also as Judo frequently remarks, there should be much more formal incentive for publishing replication studies and negative results.
It seems kind of obvious that peer review is going to reward peer think, peer citation, and academic incremental advance. Obviously that's not how innovation works.
the system, as flawed as it is, is very effective for its purpose. see eg "success is 10% inspiration and 90% perspiration". on a darker side, the purpose is not to be fair to any particular individual, or even to be conducive to human flourishing at large.
There's an article I came across awhile back, that I can't easily find now, that basically mapped out the history of our current peer review system. Peer review as we know it today was largely born in the 70s and a response to several funding crises in academia. Peer review was a strategy to make research appear more credible.
The most damning critique of peer-review of course is that it completely failed to stop (and arguably aided) the reproducibility crisis. We have an academic system where the prime motivation is the secure funding through the image of credibility, which from first principles is a recipe for wide spread fraud.