Understanding and supporting anonymity policies in peer review
Bibliographic record
Abstract
Design of peer‐review support systems is shaped by the policies that define and govern the process of peer review. An important component of these are policies that deal with anonymity: The rules that govern the concealment and transparency of information related to identities of the various stakeholders (authors, reviewers, editors, and others) involved in the peer‐review process. Anonymity policies have been a subject of debate for several decades within scholarly communities. Because of widespread criticism of traditional peer‐review processes, a variety of new peer‐review processes have emerged that manage the trade‐offs between disclosure and concealment of identities in different ways. Based on an analysis of policies and guidelines for authors and reviewers provided by publication venues, we developed a framework for understanding how disclosure and concealment of identities is managed. We discuss the appropriate role of information technology and computer support for the peer‐review process within that framework.
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 | 0.264 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.033 | 0.048 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".