Current policies governing editorial conflicts of interest are ineffective
Bibliographic record
Abstract
Research-active editors face a potential conflict of interest (COI) when handling submissions from authors who share the same affiliation or those who recently collaborated with the editor. Since perception of COIs arising from such editor-author associations may erode trust in science, some policies recommend, and others demand, recusal in such incidents. However, the effectiveness of such measures is unknown to date. To fill this gap, we analyze half a million papers from six publishers who specify the handling editor of each paper. We find numerous papers with editor-author associations, and demonstrate that such papers tend to be accepted faster. A quasi-experimental design exploiting policy changes at PNAS and PLOS reveals the limited effectiveness of current COI policies. A network neural embedding model reveals that requiring editors with potential COIs to recuse may compromise the suitability of the handling editor. Finally, an online survey experiment demonstrates that such COIs influence trust in the paper's finding, but public disclosure eliminates this effect.
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 | 0.168 | 0.549 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".