Did the Research Faculty at a Small Canadian Business School Publish in “Predatory” Venues? This Depends on the Publishing Blacklist
Bibliographic record
Abstract
The first ever quantitative paper to claim that papers published in so-called “predatory” open access (OA) journals and publishers were financially remunerated emerged from Canada. That study, published in the Journal of Scholarly Publishing (University of Toronto Press) in 2017 by Derek Pyne at Thompson Rivers University, garnered wide public and media attention, even by renowned news outlets such as The New York Times and The Economist. Pyne claimed to have found that most of the human subjects of his study had published in “predatory” OA journals, or in OA journals published by “predatory” OA publishers, as classified by Jeffrey Beall. In this paper, we compare the so-called “predatory” publications referred to in Pyne’s study with Walt Crawford’s gray open access (grayOA) list, as well as with Cabell’s blacklist, which was introduced in 2017. Using Cabell’s blacklist and Crawford’s grayOA list, we found that approximately 2% of the total publications (451) of the research faculty at the small business school were published in potentially questionable journals, contrary to the Pyne study, which found significantly more publications (15.3%). In addition, this research casts doubt to the claim made in Pyne’s study that research faculty members who have predatory publications have 4.3 “predatory” publications on average.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".