同行评审语气的差异与评审人匿名性的作用
Disparities in Peer Review Tone and the Role of Reviewer Anonymity
- New York University Abu Dhabi（纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过对两本主要期刊超过80,000条评审意见进行自然语言处理和大规模统计建模，揭示评审语气、情感和支持性语言随作者性别、种族及机构归属而变化的差异，并考察评审人匿名与署名对评价语言的影响，从而暴露同行评审中的隐性偏见并质疑匿名性在公平性中的传统作用。
AI中文摘要：
同行评审过程常被视为科学诚信的守门人，然而越来越多的证据表明，它并不能免于偏见。尽管同行评审中的结构性不平等已被广泛讨论，但语言本身可能以微妙方式强化差异的问题却远未受到足够关注。本研究开展了迄今为止最全面的同行评审语言分析之一，考察了两本主要期刊中超过80,000条评审意见。利用自然语言处理和大规模统计建模，研究揭示了评审语气、情感和支持性语言如何因作者的人口统计学特征（包括性别、种族和机构归属）而变化。通过使用同时包含匿名评审和署名评审的数据集，本研究还揭示了评审人身份的披露如何塑造评价语言。研究结果不仅暴露了同行反馈中隐藏的偏见，也对关于匿名性在公平性中所起作用的传统假设提出了挑战。在学术出版面临改革之际，这些见解提出了关于评审政策如何塑造职业发展轨迹和科学进步的关键问题。
英文摘要：
The peer review process is often regarded as the gatekeeper of scientific integrity, yet increasing evidence suggests that it is not immune to bias. Although structural inequities in peer review have been widely debated, much less attention has been paid to the subtle ways in which language itself may reinforce disparities. This study undertakes one of the most comprehensive linguistic analyses of peer review to date, examining more than 80,000 reviews in two major journals. Using natural language processing and large-scale statistical modeling, it uncovers how review tone, sentiment, and supportive language vary across author demographics, including gender, race, and institutional affiliation. Using a data set that includes both anonymous and signed reviews, this research also reveals how the disclosure of reviewer identity shapes the language of evaluation. The findings not only expose hidden biases in peer feedback, but also challenge conventional assumptions about anonymity's role in fairness. As academic publishing grapples with reform, these insights raise critical questions about how review policies shape career trajectories and scientific progress.
