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同行评审使历史上被排斥的群体面临障碍。

Peer review perpetuates barriers for historically excluded groups.

机构信息

Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA.

Department of Integrative Biology, Michigan State University, East Lansing, MI, USA.

出版信息

Nat Ecol Evol. 2023 Apr;7(4):512-523. doi: 10.1038/s41559-023-01999-w. Epub 2023 Mar 13.

Abstract

Peer review is central to the scientific process and scientists' career advancement, but bias at various stages of the review process disadvantages some authors. Here we use peer review data from 312,740 biological sciences manuscripts across 31 studies to (1) examine evidence for differential peer review outcomes based on author demographics, (2) evaluate the efficacy of solutions to reduce bias and (3) describe the current landscape of peer review policies for 541 ecology and evolution journals. We found notably worse review outcomes (for example, lower overall acceptance rates) for authors whose institutional affiliations were in Asia, for authors whose country's primary language is not English and in countries with relatively low Human Development Indices. We found few data evaluating efficacy of interventions outside of reducing gender bias through double-blind review or diversifying reviewer/editorial boards. Despite evidence for review outcome gaps based on author demographics, few journals currently implement policies intended to mitigate bias (for example, 15.9% of journals practised double-blind review and 2.03% had reviewer guidelines that mentioned social justice issues). The lack of demographic equity signals an urgent need to better understand and implement evidence-based bias mitigation strategies.

摘要

同行评议是科学过程和科学家职业发展的核心,但评审过程的各个阶段存在偏见,这对一些作者不利。在这里,我们使用来自 31 项研究中 312740 篇生物科学手稿的同行评议数据:(1) 检验基于作者人口统计学特征的差异同行评议结果的证据;(2) 评估减少偏见的解决方案的效果;(3) 描述 541 种生态学和进化期刊的同行评议政策的现状。我们发现,机构隶属关系在亚洲的作者、母语不是英语的作者以及人类发展指数相对较低的国家的作者的审稿结果明显较差(例如,总体接受率较低)。我们发现,除了通过双盲评审减少性别偏见之外,很少有数据评估干预措施的效果,很少有期刊目前实施旨在减轻偏见的政策(例如,15.9%的期刊实行双盲评审,2.03%的期刊有评审员指南提到社会公正问题)。缺乏人口统计学公平的现象表明,迫切需要更好地理解和实施基于证据的减轻偏见的策略。

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