“Refereeing Template”: A Guide to Writing an Effective Peer Review
Bibliographic record
Abstract
We offer here a “Refereeing Template” to guide a more constructive and systematic peer review process. This template provides the following algorithm for constructing a peer review: (i) list the key claims of the manuscript; (ii) determine if the data (and interpretations of the data) support said claims; and, only as a last step, (iii) offer an opinion as to whether the supported claims deserve publication in the targeted journal. The Refereeing Template is designed to improve the quality and clarity of reviews by emphasizing claims, arguments backed by supporting evidence, as the basis for evaluating the quality of a manuscript, and translating that evaluation into an assessment of impact and significance.
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 | 0.270 | 0.507 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.153 | 0.195 |
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".