Reply to: Recognizing and marshalling the pre-publication error correction potential of open data for more reproducible science
Bibliographic record
Abstract
In a previous paper, we demonstrated a lack of association between open data requirements and post-publication error correction among journals in ecology and evolution 1 .To facilitate effective data review and error correction, we recommended improving the archiving quality of open datasets, sharing analytical code alongside datasets, and destigmatizing error correction among researchers and journal editors.In response to our paper, Chen et al. 2 highlighted that mandatory open data policies also increase opportunities for detecting and correcting errors pre-publication.We welcome Chen et al.'s comment and acknowledge that we omitted discussing the important, positive impact that mandatory open data policies can have on various pre-publication processes.Our study design and the interpretation of our results were likely influenced by our prior experience of reporting data anomalies and research misconduct to journals, and witnessing first-hand the challenges of post-publication error correction 3-5 .As longstanding advocates of transparency and reproducibility in research, we would celebrate empirical evidence that data sharing mandates increase pre-publication error detection.
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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 | 0.023 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.043 | 0.059 |
| Insufficient payload (model declined to judge) | 0.007 | 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".