Elsevier is certainly evil, but I would say the root issue is the practices of the institutions where these "authors" are employed. This kind of thing is academic misconduct and should result in them losing their jobs.
Goodhart's law states "When a measure becomes a target, it ceases to be a good measure", and that's what we see here. There is a strong incentive to publish more instead of better. Ideas are spread into multiple papers, people push to be listed as authors, citations are fought for, and some become dishonest and start with citation cartels, "hidden" citations in papers (printed small in white-on-white, meaning it's indexed by citation crawlers but not visible to reviewers) and so forth.
This also destroys the peer review system upon which many venues depend. Peer reviews were never meant to catch cheaters. The huge number of low-to-medium quality papers in some fields (ML, CV) overworks reviewers, leading to things like CVPR forcing authors to be reviewers or face desk rejection. AI papers, AI reviews of dubious quality slice in even more.
Ultimately the only true fix for this is to remove the incentives. Funding and careers should no longer depend on the sheer number of papers and citations. The issue is that we have not really found anything better yet.
There are lots of better things, like people making hiring and firing decisions based on their evaluation of the content of papers they have actually read, instead of just a number. If someone is publishing so many papers that a hiring committee can't even read a meaningful fraction of them, that should be a red flag in itself, rather than a green one.
It will just pick the best allocation metric it has available, even if that metric would never stand up to scrutiny in the private sector, or any more directly measured domain, public or private.
A distressingly high percentage of humans like zero-sum status games. More people are happier when status is recognized as a semi-unbounded positive-sum game.
There’s not a whole lot to gain for the individual or even the institution unless they hit an absolute home run on the first try that also shows positive results very quickly. More than likely the decision will be questioned at every turn
Publishing uninteresting science for the record is different from an incentive to go against the crowd to refute incorrect claims.
Both would be good especially these days.
I’d even argue that still today women and minorities are strongly disadvantaged at many institutions. I’d say that as a white male that recently left academia myself. I have seen how some of my colleagues have been treated.
Oh boy, you seem to be missing the forest for the trees. When science was a hobby of the rich, there was no need to measure output. Only when "scientist" became a career and these scientists started demanding government funding (which only really crystallized in the 20th century), then we started needing a way to measure output.
You could try doing away with an objective measure of academic output and replace it with the "social fabric of researchers and institutes" (whatever the fuck that means) instead , but then all you'd have is a good ol' boys club funded by taxpayer money.
That said, as far as I'm aware those metrics aren't explicitly considered by said panels (NIH for example). Any issue in that regard is presumably due to either unconscious bias or laziness on the part of said experts when exposed to such metrics.
I agree it's not perfect but that's still several steps removed from "Billy is one of us, he should get that tenured position" and, as this article shows, it requires openly unethical behavior, which others can recognize and eventually prosecute (even if that isn't being done often enough).
It's almost like saying "well corruption happens anyways so why do we even criminalize it and have public hearings? Just skip those bits and openly auction votes instead".
I think most interviews can essentially be described as "Billy is one of us, he should get position X" if one is feeling cynical.
That would be one way to implement his suggestion of getting rid of objective (if flawed) measures of a researcher's performance.
I agree that, inevitably, there will be a subjective human decision in there, but I argue that dropping all objective measures of performance and going just on vibes is kicking the door wide open for corruption, while it's merely cracked right now. And the exploit mentioned in this article is a very public and explicit one, which is why other researchers were so aware of it and it eventually caught up to him. If it all gets moved behind closed doors instead, it will be even harder to detect and prosecute this sort of behavior.
The decision makers who are the target audience for these metrics value "objective" data. They value the appearance of being quantitative, but lack the intellectual tools to distinguish between quantitative science and pseudoscience with numbers bolted on.
That's modern bureaucracy in a nutshell.