Editors are biased too: An extension of Fox et al. (2023)'s analysis makes the case for triple‐blind review
Bibliographic record
Abstract
Abstract Functional Ecology conducted a randomised trial comparing single‐ and double‐blind peer review; a recent analysis of this data found substantial evidence for bias by reviewers. We show that this dataset can also be analysed for editor bias, after controlling for both reviewer bias and paper quality. Our analysis shows that editors tend to be more likely to invite high‐scoring manuscripts for revision or resubmission when the first author is a man from a country with a very high Human Development Index (HDI); first authors who were women or not from very high HDI countries were more likely to be rejected at this stage. We propose that journals consider a triple‐blind review process where neither editors nor reviewers know the identity of authors, and authors do not know the identity of reviewers nor editors. Read the free Plain Language Summary for this article on the Journal blog.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.565 | 0.832 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".