Publisher and journal reciprocity for peer review: Not so much
Bibliographic record
Abstract
Peer reviewers provide a critical role in helping journals keep publishing. To understand the rewards and incentives offered to peer reviewers, we assessed what journals/publishers offered to one peer reviewer in biomedicine over a 1-month period (June 2023). After receiving 88 peer reviewer invitations, we noted that incentives were minimal. They include access to journal/publisher peer review training materials, reduced author processing charges of future article submissions, and free access to the journal/publisher website. Depending on the acceptance rate (30% or 50%) of recommendations to publish the article, peer review from this sample could generate anywhere from $USD 897,000 to $USD 1.45 million dollars when annualized. However, little, if any of this revenue is shared directly or indirectly with peer reviewers. With almost no reciprocity in the peer review process, journals and their publishers need to promote and establish more reciprocity in a system that currently largely favors them disproportionately. This study is an anecdotal perspective of one peer reviewer's experience over a single month. While anecdotal, these findings highlight issues about the fairness and sustainability of the peer review system. We encourage others to expand on what we have done and include more empirical investigations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.269 | 0.620 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".