Global Research Trends in Predatory Publishing: A Bibliometric and Topic Analysis
Bibliographic record
Abstract
More than a decade has passed since researchers began discussing predatory publishing, one of the most unethical practices in academia. Nevertheless, few attempts have been made to provide a comprehensive overview of the research on predatory publishing. Therefore, this study conducted bibliometric and topic analysis on 812 papers collected from the Web of Science database. The results showed that, although the annual publication volume decreased slightly in the last two years, the annual citations continued to rise. The United States and its institutions are global leaders in predatory publishing research. The most active journal was Learned Publishing, whereas the most influential was Nature. Furthermore, eleven research topics regarding predatory publishing, academic publishing, or the research community were identified and interpreted. Based on these results, this study discusses agendas such as highlighting the global interest in the issue, the need for large-scale collaboration and a sustainable research environment, promoting the issue of predatory publishing, and the importance of education for stakeholders. The findings are expected to help researchers and policymakers understand global research trends in predatory publishing.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.111 | 0.166 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".