Costly collaborations: The impact of scientific fraud on co‐authors' careers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past few years, several major scientific fraud cases have shocked the scientific community. The number of retractions each year has also increased tremendously, especially in the biomedical field, and scientific misconduct accounts for more than half of those retractions. It is assumed that co‐authors of retracted papers are affected by their colleagues' misconduct, and the aim of this study is to provide empirical evidence of the effect of retractions in biomedical research on co‐authors' research careers. Using data from the W eb of S cience, we measured the productivity, impact, and collaboration of 1,123 co‐authors of 293 retracted articles for a period of 5 years before and after the retraction. We found clear evidence that collaborators do suffer consequences of their colleagues' misconduct and that a retraction for fraud has higher consequences than a retraction for error. Our results also suggest that the extent of these consequences is closely linked with the ranking of co‐authors on the retracted paper, being felt most strongly by first authors, followed by the last authors, with the impact is less important for middle authors.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it