Evidence map›Paper›PMID 40173178›Full record

Trial reportPloS one2025

Peer reviews of peer reviews: A randomized controlled trial and other experiments.

Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, Alice Oh, Alekh Agarwal, Danielle Belgrave, Nihar B Shah

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Systematization of peer review in Epidemiologia e Serviços de Saúde.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alexander GoldbergSchool of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.ORCID 0000-0003-1818-6387
Ivan StelmakhNew Economic School, Moscow, Russia.
Kyunghyun ChoCenter for Data Science, New York University, New York, New York, United States of America.
Alice OhSchool of Computing, KAIST, Daejeon, Republic of Korea.
Alekh AgarwalGoogle Research, United States of America.
Danielle BelgraveGSK, London, United Kingdom.
Nihar B ShahSchool of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Is it possible to reliably evaluate the quality of peer reviews? We study this question driven by two primary motivations - incentivizing high-quality reviewing using assessed quality of reviews and measuring changes to review quality in experiments. We conduct a large scale study at the NeurIPS 2022 conference, a top-tier conference in machine learning, in which we invited (meta)-reviewers and authors to voluntarily evaluate reviews given to submitted papers. First, we conduct a randomized controlled trial to examine bias due to the length of reviews. We generate elongated versions of reviews by adding substantial amounts of non-informative content. Participants in the control group evaluate the original reviews, whereas participants in the experimental group evaluate the artificially lengthened versions. We find that lengthened reviews are scored (statistically significantly) higher quality than the original reviews. Additionally, in analysis of observational data we find that authors are positively biased towards reviews recommending acceptance of their own papers, even after controlling for confounders of review length, quality, and different numbers of papers per author. We also measure disagreement rates between multiple evaluations of the same review of 28% - 32%, which is comparable to that of paper reviewers at NeurIPS. Further, we assess the amount of miscalibration of evaluators of reviews using a linear model of quality scores and find that it is similar to estimates of miscalibration of paper reviewers at NeurIPS. Finally, we estimate the amount of variability in subjective opinions around how to map individual criteria to overall scores of review quality and find that it is roughly the same as that in the review of papers. Our results suggest that the various problems that exist in reviews of papers - inconsistency, bias towards irrelevant factors, miscalibration, subjectivity - also arise in reviewing of reviews.

Indexed as

Peer ReviewPeer Review, ResearchHumansMachine Learning

Identifiers

PMID40173178
PMCPMC11964232

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.