Suppr超能文献

性别偏见影响最优秀的候选人。

Gender Bias Impacts Top-Merited Candidates.

作者信息

Andersson Emma Rachel, Hagberg Carolina E, Hägg Sara

机构信息

Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.

Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

出版信息

Front Res Metr Anal. 2021 May 10;6:594424. doi: 10.3389/frma.2021.594424. eCollection 2021.

Abstract

Expectations of fair competition underlie the assumption that academia is a meritocracy. However, bias may reinforce gender inequality in peer review processes, unfairly eliminating outstanding individuals. Here, we ask whether applicant gender biases peer review in a country top ranked for gender equality. We analyzed peer review assessments for recruitment grants at a Swedish medical university, Karolinska Institutet (KI), during four consecutive years (2014-2017) for Assistant Professor ( = 207) and Senior Researcher ( = 153). We derived a composite bibliometric score to quantify applicant productivity and compared this score with subjective external (non-KI) peer reviewer scores of applicants' merits to test their association for men and women, separately. To determine whether there was gender segregation in research fields, we analyzed publication list MeSH terms, for men and women, and analyzed their overlap. There was no gendered MeSH topic segregation, yet men and women with equal merits are scored unequally by reviewers. Men receive external reviewer scores resulting in stronger associations (steeper slopes) between computed productivity and subjective external reviewer scores, meaning that peer reviewers "reward" men's productivity with proportional merit scores. However, women applying for assistant professor or senior researcher receive only 32 or 92% of the score men receive, respectively, for each additional composite bibliometric score point. As productivity increases, the differences in merit scores between men and women increases. Accumulating gender bias is thus quantifiable and impacts the highest tier of competition, the pool from which successful candidates are ultimately chosen. Track record can be computed, and granting organizations could therefore implement a computed track record as quality control to assess whether bias affects reviewer assessments.

摘要

对公平竞争的期望是学术界是精英管理体制这一假设的基础。然而,偏见可能会在同行评审过程中加剧性别不平等,不公平地淘汰优秀个体。在此,我们探讨在一个性别平等排名居首的国家,申请人的性别是否会影响同行评审。我们分析了瑞典卡罗林斯卡学院(KI)连续四年(2014 - 2017年)助理教授(n = 207)和高级研究员(n = 153)招聘资助的同行评审评估。我们得出一个综合文献计量得分来量化申请人的生产力,并将该得分与外部(非KI)同行评审员对申请人优点的主观评分进行比较,分别测试男性和女性之间的关联。为了确定研究领域是否存在性别隔离,我们分析了男性和女性的出版物列表医学主题词(MeSH),并分析了它们的重叠情况。不存在按性别划分的MeSH主题隔离,但优点相同的男性和女性得到的评审员评分却不相等。男性获得的外部评审员评分使得计算出的生产力与主观外部评审员评分之间的关联更强(斜率更陡),这意味着同行评审员用成比例的优点分数“奖励”男性的生产力。然而,申请助理教授或高级研究员职位的女性,每增加一个综合文献计量得分点,分别只能获得男性得分的32%或92%。随着生产力的提高,男性和女性在优点分数上的差异也会增加。因此,累积的性别偏见是可量化的,并且会影响最高层级竞争,即最终选出成功候选人的人才库。可以计算过往记录,因此资助机构可以将计算出的过往记录作为质量控制措施,以评估偏见是否影响评审员的评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e89/8141636/2b45bd00b95e/frma-06-594424-g0001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验