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You hardly need AI for this; you only need to construct a graph of researchers with the number of citations as weights, and compare metrics of connectedness to

You hardly need AI for this; you only need to construct a graph of researchers with the number of citations as weights, and compare metrics of connectedness to detect cliques and other fraudulent behavior.

Ma8ee 2y ago
Cliques can easily occur just by a few people researching the same thing. If you are maybe two or three research groups in the works that use a particular method to try to solve a particular problem, of course you are all going to cite each other excessively.
- deleted 2y ago
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nradov 2y ago
You're going to get a lot of false positives with such a simplistic approach. Sometimes the same group of researchers legitimately cites each other over and over because they're working on similar topics and collaborating. AI can help to reduce the false positives by looking at content to identify citations that might be out of place. Of course there will still be some false positives with any algorithm so human academic committees will still have to make the final decisions.
- ChainOfFools 2y ago
  Unfortunately a citation, especially as a prestige or impact metric, carries associations of quality to observers outside its domain. A resource having been selected for citation implicitly signals that it wad the best choice among a field of rival candidates, yet in many cases such as you describe the truth is that it is the _only_ such work available. This is especially problematic as some researchers build entire careers out of gatekeeping preprint access to unique, single- source primary data that only they or their lab/ staff can collect- not because of their superior ability to analyze it but because theirs is the only facility with the appropriate ensemble of apparatus on site or close at hand, or because they are the latest in a close-knit (often nepotistic) chain of custodians presiding over a certain exclusive primary source, such as a nature reserve or archaeological site.

Ma8ee 2y ago

Cliques can easily occur just by a few people researching the same thing. If you are maybe two or three research groups in the works that use a particular method to try to solve a particular problem, of course you are all going to cite each other excessively.

deleted 2y ago
[deleted]

nradov 2y ago

You're going to get a lot of false positives with such a simplistic approach. Sometimes the same group of researchers legitimately cites each other over and over because they're working on similar topics and collaborating. AI can help to reduce the false positives by looking at content to identify citations that might be out of place. Of course there will still be some false positives with any algorithm so human academic committees will still have to make the final decisions.

ChainOfFools 2y ago
Unfortunately a citation, especially as a prestige or impact metric, carries associations of quality to observers outside its domain. A resource having been selected for citation implicitly signals that it wad the best choice among a field of rival candidates, yet in many cases such as you describe the truth is that it is the _only_ such work available. This is especially problematic as some researchers build entire careers out of gatekeeping preprint access to unique, single- source primary data that only they or their lab/ staff can collect- not because of their superior ability to analyze it but because theirs is the only facility with the appropriate ensemble of apparatus on site or close at hand, or because they are the latest in a close-knit (often nepotistic) chain of custodians presiding over a certain exclusive primary source, such as a nature reserve or archaeological site.