Does double‐blind peer review reduce bias? Evidence from a top computer science conference
Bibliographic record
Abstract
Abstract Peer review is essential for advancing scientific research, but there are long‐standing concerns that authors' prestige or other characteristics can bias reviewers. Double‐blind peer review has been proposed as a way to reduce reviewer bias, but the evidence for its effectiveness is limited and mixed. Here, we examine the effects of double‐blind peer review by analyzing the review files of 5,027 papers submitted to a top computer science conference that changed its reviewing format from single‐ to double‐blind in 2018. First, we find that the scores given to the most prestigious authors significantly decreased after switching to double‐blind review. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance significantly. Second, the inter‐reviewer disagreement increased significantly in the double‐blind format. Third, papers rejected in the single‐blind format are cited more than those rejected under double‐blind, suggesting that double‐blind review better excludes poorer quality papers. Lastly, an apparently unrelated change in the rating scale from 10 to 4 points likely reduced prestige bias significantly such that papers' acceptance was affected. These results support the effectiveness of double‐blind review in reducing biases, while opening new research directions on the impact of peer‐review formats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.458 | 0.849 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".