Analyzing the citation impact of predatory journals in the health sciences
Bibliographic record
Abstract
Objective: Predatory journal articles do not undergo rigorous peer review and so their quality is potentially lower. Citing them disseminates the unreliable data they may contain and may undermine the integrity of science. Using citation analysis techniques, this study investigates the influence of predatory journals in the health sciences. Methods: The twenty-six journals in the "Medical Sciences" category of a known predatory publisher were selected. The number of articles published by these journals was recorded based on the information from their websites. The "Cited References" search function in Web of Science was used to retrieve citation data for these journals. Results: Of the 3,671 articles published in these predatory journals, 1,151 (31.4%) were cited at least once by 3,613 articles indexed in Web of Science. The number of articles that cited articles published in predatory journals increased significantly from 64 in 2014 to 665 in 2022, an increase of 10-fold in nine years. The citing articles were published by researchers from all over the world (from high-, middle-, and lower-income countries) and in the journals of traditional and open access publishers. Forty-three percent (1,560/3,613) of the citing articles were supported by research funds. Conclusions: The content from articles published in predatory journals has infiltrated reputable health sciences journals to a substantial extent. It is crucial to develop strategies to prevent citing such articles.
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.
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 | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.128 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.103 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".