An Empirical Study of International Retracted Research Articles of Famous Chinese Universities from 2012 to 2022
Bibliographic record
Abstract
Studying retracted research articles can help regulate and clean up the academic ecology. Given the frequent occurrence of academic misconduct in China, this study aims to profile the 1,635 retracted research articles indexed in the Retraction Watch (RW) database from 2012 to 2022 and researchers at the 22 Chinese first-class universities ranked in the top 500 by the World Times Higher Education (THE) global ranking of 2023. Descriptive statistics were used to analyse the data, including the retraction number and rate, authors, journals, publishers, subjects, retraction time lag, and reasons for academic dishonesty and misconduct in Chinese higher education. The study found that there is no positive correlation between the number of academic papers published, the number of retractions, and the retraction rate of each university. There is not much cooperation on the retracted papers, but the co-authors come from a wide range of countries; The journals with high retractions are mainly open-access journals in biomedicine indexed by Science Citation Index Expanded (SCIE), which reflects that the quality of papers in open-access journals is yet to be tested; The three major world-renowned publishers with a higher number of retractions have a greater influence in scientific publishing and handling of retracted articles; The average time lag for retraction can be as long as more than five years indicating that international journals have irregularities in the review process. The retracted papers demonstrate a profound interdisciplinary nature, but academic misconduct is still the main reason for the withdrawal of academic papers in first-class universities in China. Collaborative efforts of all the relevant parties and administrators should be made to crack down the academic misconduct taking into account the complex landscape of academic misconduct.
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How this classification was reachedexpand
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 integrityBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchBibliometricsResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".