Optimizing peer review to minimize the risk of retracting COVID-19-related literature
Bibliographic record
Abstract
Retractions of COVID-19 literature in both preprints and the peer-reviewed literature serve as a reminder that there are still challenging issues underlying the integrity of the biomedical literature. The risks to academia become larger when such retractions take place in high-ranking biomedical journals. In some cases, retractions result from unreliable or nonexistent data, an issue that could easily be avoided by having open data policies, but there have also been retractions due to oversight in peer review and editorial verification. As COVID-19 continues to affect academics and societies around the world, failures in peer review might also constitute a public health risk. The effectiveness by which COVID-19 literature is corrected, including through retractions, depends on the stringency of measures in place to detect errors and to correct erroneous literature. It also relies on the stringent implementation of open data 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 | 0.572 | 0.878 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.033 | 0.021 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.038 | 0.052 |
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".