Proposal for ‘segmented peer review’ of multidisciplinary papers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it