Comment by aleph_minus_one
1 day ago
> Instead it says that careers depended on publication counts before original research was even expected of university professors.
But these were not peer-reviewed publications.
1 day ago
> Instead it says that careers depended on publication counts before original research was even expected of university professors.
But these were not peer-reviewed publications.
True. But I'd also gladly report that in small pockets like AI, having highly cited and used arXiv preprints that never passed peer-review can also be impactful regarding careers and job offers. Industry and academics often are smarter than looking at raw numbers. And they are aware that some subfields can sometimes be overtaken by mafialike collusion rings that keep every outsider away from their turf (it's usually hyperspecific to some narrowly defined AI task or benchmark area). It also depends on whether the decision maker is some huge committee and multi-level bureaucracy with process and metrics and numbers in Excel sheets or just one professor who can make their overall assessment without being questioned on it. Of course, this relies on trust that can be abused for biased hiring. Falling back on raw metrics is often a CYA tactic in low-trust, litigious societies.