Update. These researchers built an #AI system to predict #REF #assessment scores from a range of data points, inc #citation rates. For individual works, the system was not very accurate. But for total institutional scores, it was 99.8%. "Despite this, we are not recommending this solution because in our judgement, its benefits are marginally outweighed by the perverse incentive it would generate for institutions to overvalue journal impact factors."
https://blogs.lse.ac.uk/impactofsocialsciences/2023/01/16/can-artificial-intelligence-assess-the-quality-of-academic-journal-articles-in-the-next-ref/

#DORA #JIFs #Metrics

Update. I'm sure this has occurred to #AI / #LLM tool builders. Determining whether an assertion is #true is a hard problem and we don't expect an adequate software solution any time soon, if ever. But determining whether a #citation points to a real publication and whether it's #relevant to the passage citing it, are comparatively easy. (Just comparatively.)

Some tools already cite sources. But when will tools promise that their citations are real and relevant — and deliver on that promise?

Reprise de l’origami avec cette parure de diamants aux couleurs des hortensias et cette magnifique citation pour laquelle on me donne deux auteurs différents : « ne se mettre à genou que pour cueillir une fleur ». Alors Brel ou Prévert ?
#citation #poesie #decoration #japon #delicatesse #rose #bleu #lille #artisanat