Who is Publishing in Biomedical Predatory Journals? A Study on Chinese Scholars
Bibliographic record
Abstract
The scale of predatory journals in the biomedical field is proliferating worldwide. In China, numerous cases of academic misconduct have occurred in international biomedical journals. The study aims to understand the sociodemographic characteristics of Chinese authors publishing in predatory biomedical journals and their perceptions of predatory journals. In predatory biomedical journals, 1408 Chinese scholars with 1482 published papers were identified. A questionnaire on predatory journals was emailed to them to analyse their perceptions of predatory journals. The study finds that provinces and cities with more authors are mainly distributed in eastern and central China. Authors mainly worked in hospitals ( n = 1162, 82.53 per cent) and schools ( n = 246, 17.47 per cent). Among hospitals, forty-eight are currently ranked in the top fifty in China. A total of ninety-three (7 per cent) authors responded to the questionnaire. Only half of the authors knew the concept of predatory journals ( n = 45, 48.39 per cent). Most respondents would not consider choosing predatory journals again ( n = 85, 91.40 per cent). Among all the corresponding authors, doctors working in top Chinese hospitals made up the majority. Chinese authors had insufficient knowledge of predatory journals, although most had professional expertise.
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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 | MetaresearchBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchBibliometricsResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.276 | 0.609 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.216 | 0.333 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.418 | 0.266 |
| Open science | 0.012 | 0.002 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".