Research Article: “Gender and International Diversity Improves Equity in Peer Review” (Preprint)

The following article (preprint) was posted today on bioRxiv.
Title
Gender and International Diversity Improves Equity in Peer Review
Authors
Dakota Murray
Indiana University Bloomington

Kyle Siler
Utrech University
Vincent Lariviére
Université de Montréal

Wei Mun Chan
eLife Sciences Publishing
Andrew M. Collings
eLife Sciences Publishing

Jennifer Raymond
Stanford University
Cassidy R Sugimoto
Indiana University Bloomington

Source
via bioRxiv
August 29, 2018
doi: 10.1101/400515
Submitted to PLOS Biology
Abstract

The robustness of scholarly peer review has been challenged by evidence of disparities in publication outcomes based on author’s gender and nationality. To address this, we examine the peer review outcomes of 23,873 initial submissions and 7,192 full submissions that were submitted to the biosciences journal eLife between 2012 and 2017. Women and authors from nations outside of North America and Europe were underrepresented both as gatekeepers (editors and peer reviewers) and last authors. We found a homophilic interaction between the demographics of the gatekeepers and authors in determining the outcome of peer review; that is, gatekeepers favor manuscripts from authors of the same gender and from the same country. The acceptance rate for manuscripts with male last authors was significantly higher than for female last authors, and this gender inequity was greatest when the team of reviewers was all male; mixed-gender gatekeeper teams lead to more equitable peer review outcomes. Similarly, manuscripts were more likely to be accepted when reviewed by at least one gatekeeper with the same national affiliation as the corresponding author. Our results indicated that homogeneity between author and gatekeeper gender and nationality is associated with the outcomes of scientific peer review. We conclude with a discussion of mechanisms that could contribute to this effect, directions for future research, and policy implications.

Code and anonymized data have been made available at https://github.com/murrayds/elife-analysis
Direct to Full Text Article (Preprint)
33 pages; PDF.

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