An Analysis of Recently Retracted Articles by Authors Affiliated with Hospitals in Mainland China
Bibliographic record
Abstract
The aim of this study was to analyze the features of retracted articles by authors affiliated with hospitals in mainland China. We searched the PubMed, Web of Science, and Retraction Watch databases for retractions and identified the following characteristics of each retracted article: publisher, open access status, impact factor of the journal that retracted the article, any PubPeer comments recorded before the retraction, status of the hospital where the authors worked, and any response to the retraction from the authors. We found 521 retractions, primarily by authors at grade A, third-level hospitals located in a limited number of regions of mainland China, and found that the journals that had published and later retracted the articles tended to have a medium to high impact factor. The main reasons for retraction were data manipulation, fabrication, or fraud; errors made by the authors; or plagiarism. Few of the retracted publications had PubPeer comments before their retraction. This is the first report to focus on retracted research coming out of hospitals in mainland China. The large number of retractions for Chinese hospitals is worrying. The results suggest that some retractions were related to third parties that provided editorial and other services.
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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 | 0.027 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".