Reviewer selection biases editorial decisions on manuscripts
Bibliographic record
Abstract
Abstract Many journals, including the Journal of Neurochemistry , enable authors to list peer reviewers as ‘preferred’ or ‘opposed’ suggestions to the editor. At the Journal of Neurochemistry , the handling editor ( HE ) may follow recommendations or select non‐author‐suggested reviewers (non‐ ASR s). We investigated whether selection of author‐suggested reviewers ( ASR s) influenced decisions on a paper, and whether differences might be related to a reviewer’s, editor's or manuscript's geographical location. In this retrospective analysis, we compared original research articles submitted to the Journal of Neurochemistry from 2013 through 2016 that were either reviewed exclusively by non‐ ASR s, by at least one ASR , by at least one reviewer marked by the author as ‘opposed’ or none. Manuscript outcome, reviewer rating of manuscript quality, rating of the reviewers’ performance by the editor (R‐score), time to review, and the country of the editor, reviewers and manuscript author were analyzed using non‐parametric rank‐based comparisons, chi‐square (χ 2 ) analysis, multivariate linear regression, one‐way analysis of variance, and inter‐rater reliability determination. Original research articles that had been reviewed by at least one ASR stood a higher chance of being accepted (525/1006 = 52%) than papers that had been reviewed by non‐ ASR s only (579/1800 = 32%). An article was 2.4 times more likely to be accepted than rejected by an ASR compared to a non‐ ASR (Pearson's χ 2 (1) = 181.3, p < 0.05). At decision, the editor did not simply follow the reviewers’ recommendation but had a balancing role: Rates of recommendation from reviewers for rejection were 11.2% (139/1241) with ASR s versus 29.0% (1379/4755) with non‐ ASR s (this is a ratio of 0.39 where 1 means no difference between rejection rates for both groups), whereas the proportion of final decisions to reject was 24.7% (248/1006) versus 45.7% (822/1800) (a ratio of 0.54, considerably closer to 1). Recommendations by non‐ ASR s were more favorable for manuscripts from USA /Canada and Europe than for Asia/Pacific or Other countries. ASR s judged North American manuscripts most favorably, and judged papers generally more positively (mean: 2.54 on a 1–5 scale) than did non‐ ASR s (mean: 3.16) reviewers, whereas time for review (13.28 vs. 13.20 days) did not differ significantly between these groups. We also found that editors preferably assigned reviewers from their own geographical region, but there was no tendency for reviewers to judge papers from their own region more favorably. Our findings strongly confirm a bias toward lower rejection rates when ASR s assess a paper, which led to the decision to abandon the option to recommend reviewers at the Journal of Neurochemistry . Open Data: Materials are available on https://osf.io/jshg7/ image
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".