With Great Power Comes Great Responsibility: the Importance of Rejection, Power, and Editors in the Practice of Scientific Publishing
Bibliographic record
Abstract
Peer review is an important element of scientific communication but deserves quantitative examination. We used data from the handling service manuscript Central for ten mid-tier ecology and evolution journals to test whether number of external reviews completed improved citation rates for all accepted manuscripts. Contrary to a previous study examining this issue using resubmission data as a proxy for reviews, we show that citation rates of manuscripts do not correlate with the number of individuals that provided reviews. Importantly, externally-reviewed papers do not outperform editor-only reviewed published papers in terms of visibility within a 5-year citation window. These findings suggest that in many instances editors can be all that is needed to review papers (or at least conduct the critical first review to assess general suitability) if the purpose of peer review is to primarily filter and that journals can consider reducing the number of referees associated with reviewing ecology and evolution papers.
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 | 0.409 | 0.844 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".