Bad smells in reviewers' reports? Text-mining the MDPI Open Peer Review Corpus

Gilles Hubert (1, 2) , Guillaume Cabanac (1, 2, 3) , Cyril Labbé (4, 5)

1 IRIT-IRIS - Recherche d’Information et Synthèse d’Information
2 UT3 - Université Toulouse III - Paul Sabatier
3 IUF - Institut universitaire de France
4 SIGMA - Systèmes d’Information - inGénierie et Modélisation Adaptables
5 UGA - Université Grenoble Alpes

Malpractice affecting the reviewing process is detrimental to science. We introduce methods to reveal evidence of peer review manipulation, such as template usage, citation manipulations or botched and meaningless reviewer reports. We apply and evaluate these methods on a corpus of reports.

Mots clés: Scientific literature; Peer review; MDPI

Domaines: Bibliothèque électronique [cs.DL]

Gilles Hubert, Guillaume Cabanac, Cyril Labbé. Bad smells in reviewers' reports? Text-mining the MDPI Open Peer Review Corpus. 2023. ⟨hal-04311568⟩