Bibliographic record
Abstract
Introduction: Predatory journals have been acknowledged as an increasing concern in the scholarly literature over the last decade, but research on the subject has been sparse. Research that has focused on predatory journals in the Canadian context has been even rarer, and limited to work focused on a single university. This study explores publishing trends in predatory journals by authors affiliated with Canadian universities. Methods: Articles published by authors at 30 Canadian universities, including all universities in the U15, were pulled from select predatory journals. Key data including author affiliation, article type, discipline, and grant information were extracted from the articles. Results: All universities in the study were found to have publications in predatory journals. The health sciences accounted for 72% of the publications, and the sciences for 20%. Research articles accounted for 50% of the articles. Opinion, editorial, or commentary pieces accounted for 24% and 19% were review articles. Grant funding was indicated in 34% of the articles, with NSERC and CIHR being top funders. The research-intensive U15 universities were found to publish more in predatory journals than their non-U15 compatriots, even when the universities were of similar size. Discussion: Canadian scholars were found to publish in predatory journals, particularly those scholars from the health sciences and research-intensive U15 universities. Grant funding was common, and often came from high profile funders like NSERC and CIHR. This study suggests that policy and education initiatives may be warranted in Canadian contexts, especially in the health sciences and at research-intensive universities.
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
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 | 0.013 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.044 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".