Data from: Imbalance in individual researcher’s peer review activities quantified for four British Ecological Society Journals, 2003-2010
Bibliographic record
Abstract
Researchers contribute to the scientific peer review system by providing reviews, and “withdraw” from it by submitting manuscripts that are subsequently reviewed. So far as we are aware, there has been no quantification of the balance of individual's contributions and withdrawals. We compared the number of reviews provided by individual researchers (i.e., their contribution) to the number required by their submissions (i.e. their withdrawals) in a large and anonymised database provided by the British Ecological Society. The database covered the Journal of Ecology, Journal of Animal Ecology, Journal of Applied Ecology, and Functional Ecology from 2003–2010. The majority of researchers (64%) did not have balanced contributions and withdrawals. Depending on assumptions, 12% to 44% contributed more than twice as much as required; 20% to 52% contributed less than half as much as required. Balance, or lack thereof, varied little in relation to the number of years a researcher had been active (reviewing or submitting). Researchers who contributed less than required did not lack the opportunity to review. Researchers who submitted more were more likely to accept invitations to review. These finding suggest overall that peer review of the four analysed journals is not in crisis, but only due to the favourable balance of over- and under-contributing researchers. These findings are limited to the four journals analysed, and therefore cannot include researcher's other peer review activities, which if included might change the proportions reported. Relatively low effort was required to assemble, check, and analyse the data. Broader analyses of individual researcher's peer review activities would contribute to greater quality, efficiency, and fairness in the peer review system.
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 | 0.014 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".