The missing metric: quantifying contributions of reviewers
Bibliographic record
Abstract
The number of contributing reviewers often outnumbers the authors of publications. This has led to apathy towards reviewing and the conclusion that the peer-review system is broken. Given the trade-offs between submitting and reviewing manuscripts, reviewers and authors naturally want visibility for their efforts. While study after study has called for revolutionizing publication practices, the current paradigm does not recognize reviewers' time and expertise. We propose the R-index as a simple way to quantify scientists' contributions as reviewers. We modelled its performance using simulations based on real data to show that early-mid career scientists, who complete high-quality reviews of longer manuscripts within their field, can perform as well as leading scientists reviewing only for high-impact journals. By giving citeable academic recognition for reviewing, R-index will encourage more participation with better reviews, regardless of the career stage. Moreover, the R-index will allow editors to exploit scores to manage and improve their review team, and for journals to promote high average scores as signals of a practical and efficient service to authors. Peer-review is a pervasive necessity across disciplines and the simple utility of this missing metric will credit a valuable aspect of academic productivity without having to revolutionize the current peer-review system.
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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 | 0.079 | 0.404 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".