Bibliographic record
Abstract
Peer review is an established and perhaps essential part of the pubIishing process. ln this study , 1214 review comments by 216 reviewers were analyzed to determine the nature of the reviews and to get a sense of the reviewers by developing profiles based on the nature of the comments. The reviews were categorized according to tone (complimentary, constructive, or negative); direction (specific suggestions for change versus statements of criticism); and focus (the particular aspect of the manuscript which the reviewer selected for comment). In general, the topic/content and methodology were most likely to be selected for comment, which was usually negative. Whereas authors of accepted manuscripts were often given specific suggestions for change, there was little encouragement for the authors of rejected manuscripts. Four reviewer profiles were delineated: critic, conciliator, competitor, and procrastinator. As might be expected, the reviewer profiles were interrelated with the nature of the reviewer comments. Implications for authors, editors, and reviewers are suggested.
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 | 0.115 | 0.440 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.161 | 0.160 |
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".