Proposal for ‘segmented peer review’ of multidisciplinary papers
Bibliographic record
Abstract
We propose a new process for peer review of multidisciplinary journal submissions called 'segmented peer review'. The current translational research environment increasingly requires complex and multidisciplinary studies that span multiple distinct specialties within a single paper. Such papers present logistic and practical barriers to effective peer review. To address these barriers, papers undergoing segmented peer review require authors to explicitly i) identify each of the areas of expertise required to review the paper, ii) direct each reviewer to the relevant portions of the manuscript, and iii) suggest in-field reviewers. This segmentation of the paper is then followed by a 'segmented peer review request' tailored to the expertise of each potential reviewer, with a request to confine his / her review to those 'in-scope' aspects of the paper, while de-emphasizing any optional 'out-of-scope' comments. Each reviewer indicates the fitness for publication, or suitability for revision, of their particular segment of the manuscript. The segmented peer review process is completed when the editors integrate the segmented peer reviews. We propose segmented peer review as a fit-for-purpose process with tangible advantages for authors, reviewers, and journal editors. It should reduce the specific barriers to publication inherent in the evaluation of multidisciplinary research efforts.
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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 | 0.359 | 0.447 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.034 | 0.036 |
| Insufficient payload (model declined to judge) | 0.009 | 0.018 |
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".