ArticleeLife2020
Large-scale language analysis of peer review reports.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Gender and disciplinary differences in grant proposal peer review: Content and sentiment in 39,280 reports.PloS one · 2026Article
- Does the disconnect between the peer-reviewed label and reality explain the peer review crisis, and can open peer review or preprints resolve it? A narrative review.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Open Science at the generative AI turn: An exploratory analysis of challenges and opportunities.Quantitative science studies · 2025Article
- Relationship between journal impact factor and the thoroughness and helpfulness of peer reviews.PLoS biology · 2023Article
- The researchers using AI to analyse peer review.Nature · 2022Article
- Does peer review improve the statistical content of manuscripts? A study on 27 467 submissions to four journals.Royal Society open science · 2022Article
- Article
- Gender gap in journal submissions and peer review during the first wave of the COVID-19 pandemic. A study on 2329 Elsevier journals.PloS one · 2021Article
- Article
Corrections and comments
- Commented on by
Authors and funding
5 authors.
Funding
Abstract
Peer review is often criticized for being flawed, subjective and biased, but research into peer review has been hindered by a lack of access to peer review reports. Here we report the results of a study in which text-analysis software was used to determine the linguistic characteristics of 472,449 peer review reports. A range of characteristics (including analytical tone, authenticity, clout, three measures of sentiment, and morality) were studied as a function of reviewer recommendation, area of research, type of peer review and reviewer gender. We found that reviewer recommendation had the biggest impact on the linguistic characteristics of reports, and that area of research, type of peer review and reviewer gender had little or no impact. The lack of influence of research area, type of review or reviewer gender on the linguistic characteristics is a sign of the robustness of peer review.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.