Large-scale language analysis of peer review reports.

ELife
Ivan BuljanAna Marušić

Abstract

Peer review is often criticized for being flawed, subjective and biased, but research into peer review has been hindered by a lack of access to peer review reports. Here we report the results of a study in which text-analysis software was used to determine the linguistic characteristics of 472,449 peer review reports. A range of characteristics (including analytical tone, authenticity, clout, three measures of sentiment, and morality) were studied as a function of reviewer recommendation, area of research, type of peer review and reviewer gender. We found that reviewer recommendation had the biggest impact on the linguistic characteristics of reports, and that area of research, type of peer review and reviewer gender had little or no impact. The lack of influence of research area, type of review or reviewer gender on the linguistic characteristics is a sign of the robustness of peer review.

References

Apr 22, 2009·Journal of Personality and Social Psychology·Jesse GrahamBrian A Nosek
Jan 31, 2015·Science and Engineering Ethics·David B Resnik, Susan A Elmore
Jun 16, 2017·Nature·Flaminio SquazzoniAna Marušić
Feb 22, 2018·PloS One·Francisco GrimaldoFlaminio Squazzoni
Apr 5, 2018·Proceedings of the National Academy of Sciences of the United States of America·Nikhil GargJames Zou
Sep 18, 2018·Scientometrics·Peter van den BesselaarHélène Schiffbaenker
Jan 20, 2019·Nature Communications·Giangiacomo BravoFlaminio Squazzoni
Feb 25, 2019·Mayo Clinic Proceedings·Samir HaffarM Hassan Murad
Feb 27, 2020·Nature·Flaminio SquazzoniMichael Willis
Jul 16, 2018·PeerJ. Computer Science·Lucía Santamaría, Helena Mihaljević

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Citations

Apr 25, 2021·Research in Social & Administrative Pharmacy : RSAP·Spencer E Harpe

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Software Mentioned

- guesser
Gender API
Stanford CoreNLP
LIWC
R
Stanford
SentimentR
CoreNLP
Linguistic Inquiry and Word Count ( LIWC )

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