An Analysis of Recently Retracted Articles by Authors Affiliated with Hospitals in Mainland China
Why this work is in the frame
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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Web of Science, and Retraction Watch databases for retractions and identified the following characteristics of each retracted article: publisher, open access status, impact factor of the journal that retracted the article, any PubPeer comments recorded before the retraction, status of the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.
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.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.035 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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