mlsanityData to 14 Oct 2026

Disparities in Peer Review Tone and the Role of Reviewer Anonymity

arXiv 2507.14741 ↗First posted 19 Jul 2025cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 58 references that are in the map. First posted on arXiv 19 Jul 2025.

Builds on

4 of 58 references.

References · 4

  1. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
  2. 2022Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approachcited by 23
  3. 2021Does double-blind peer-review reduce bias? Evidence from a top computer science conferencecited by 8
  4. 2023China and the U.S. produce more impactful AI research when collaborating togethercited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.