Protecting peer review: Correspondence chronology and ethical analysis regarding Logothetis vs. Shmuel and Leopold
Bibliographic record
Abstract
Editors of scientific journals are ethically bound to provide a fair and impartial peer-review process and to protect the rights of contributing authors to publish research results. If, however, a dispute arises among investigators regarding data ownership and the right to publish, the ethical responsibilities of journal editors become more complex. The editors of Human Brain Mapping recently had the unusual experience of learning of an ongoing dispute regarding data-access rights pertaining to a manuscript already accepted for publication. Herein the editors describe the nature of the dispute, the steps taken to explore and resolve the conflict, and discuss the ethical principles that govern such circumstances. Drawing on this experience and with the goal of avoiding future controversies, the editors have formulated a Data Rights Policy and a Data Rights Procedure for Human Brain Mapping. Human Brain Mapping adopts this policy effective immediately and respectfully suggests that other journals consider adopting this or similar policies.
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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 | 0.051 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.024 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".