How do authors' perceptions of their papers compare with co-authors' perceptions and peer-review decisions?


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 01 12 2023
accepted: 04 03 2024
medline: 10 4 2024
pubmed: 10 4 2024
entrez: 10 4 2024
Statut: epublish

Résumé

How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,000 submitting authors and 9,000 submitted papers, we surveyed the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews. The salient results are: (1) Authors had roughly a three-fold overestimate of the acceptance probability of their papers: The median prediction was 70% for an approximately 25% acceptance rate. (2) Female authors exhibited a marginally higher (statistically significant) miscalibration than male authors; predictions of authors invited to serve as meta-reviewers or reviewers were similarly calibrated, but better than authors who were not invited to review. (3) Authors' relative ranking of scientific contribution of two submissions they made generally agreed with their predicted acceptance probabilities (93% agreement), but there was a notable 7% responses where authors predicted a worse outcome for their better paper. (4) The author-provided rankings disagreed with the peer-review decisions about a third of the time; when co-authors ranked their jointly authored papers, co-authors disagreed at a similar rate-about a third of the time. (5) At least 30% of respondents of both accepted and rejected papers said that their perception of their own paper improved after the review process. The stakeholders in peer review should take these findings into account in setting their expectations from peer review.

Identifiants

pubmed: 38598482
doi: 10.1371/journal.pone.0300710
pii: PONE-D-23-39630
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0300710

Informations de copyright

Copyright: © 2024 Rastogi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Charvi Rastogi (C)

Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Ivan Stelmakh (I)

New Economic School, Moscow, Russia.

Alina Beygelzimer (A)

Yahoo! Research, New York, New York, United States of America.

Yann N Dauphin (YN)

Google Deepmind, Montreal, Canada.

Percy Liang (P)

Department of Computer Science, Stanford University, Stanford, California, United States of America.

Jennifer Wortman Vaughan (J)

Microsoft Research, New York, New York, United States of America.

Zhenyu Xue (Z)

Independent Researcher, Shanghai, China.

Hal Daumé Iii (H)

Department of Computer Science, University of Maryland, College Park, Maryland, United States of America.

Emma Pierson (E)

Jacobs Technion-Cornell Institute, Cornell Tech, New York, New York, United States of America.

Nihar B Shah (NB)

Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Classifications MeSH