What We Still Don't Know About Peer Review
Bibliographic record
Abstract
Despite criticisms, the peer review process (PRP) is undoubtedly well established as an official and legitimated mechanism for evaluating and controlling scientific production. Although PRP has been a prominent object of study, we argue in this article that empirical research on PRP has not been addressed in a comprehensive way. Nine categories were applied to 150 empirical research articles on the topic with results revealing various gaps in empirical PRP research: (1) the research has been dedicated to the evaluation of the system rather than to the actual description of PRP as a concrete socio-discursive practice; (2) the most productive group of studies considers the multiple relationships between the sociological attributes (socio-demographic or scientometrical) of the actors (authors, reviewers, and editors) and the results of the process but does not take into account the texts exchanged by those actors; and (3) the few studies that do analyze the texts interchanged in the process do not take into account any of the variables included (such as scientometrical data, agreement, and rejection rates) in the more productive areas of the field. This lack of integration among the methodological approaches to PRP results in a partial comprehension of this important process, which determines the production and dissemination of an important part of scientific knowledge.
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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 | 0.192 | 0.646 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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".