Bibliographic record
Abstract
1editorial peer review is an ill-defined term, and its aims are not always clearly stated. Sometimes it can refer to in-house review, but more commonly, as in this editorial, it refers to opinions gathered from external experts. Jefferson et al 1 propose that the aims of peer review “may be categorized as (1) selecting submissions for publication (by a particular journal) and rejecting those with irrelevant, trivial, weak, misleading or potentially harmful content, and (2) improving the transparency, accuracy and utility of the selected submissions.” One of the problems in assessing the various studies of peer review is the variety of study questions and end points. Nonetheless, a Cochrane review of editorial peer review concluded that “At present there is little empirical evidence to support the use of editorial peer review as a mechanism to ensure quality of biomedical research, despite its widespread use and costs.” 2 This, of course, does not mean that editorial peer review is ineffective, only that it has not been shown to be effective. Even with a consensus on detailed objectives for peer review, many methodological problems would have to be solved to assess it adequately, and the research would necessarily involve a large number of authors and editors. 1 Convincing evidence supporting the effectiveness of peer review is unlikely to appear in the near future. Meanwhile, a number of studies, although often small and not necessarily generalizable, provide guidance on some aspects of the process. Some of these studies are mentioned below.
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 | 0.330 | 0.649 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".