Bibliometric analysis of a controversial paper on predatory publishing
Bibliographic record
Abstract
Purpose In 2017, one study (Derek Pyne; Journal of Scholarly Publishing; DOI: 10.3138/jsp.48.3.137; University of Toronto Press) in the “predatory” publishing literature attracted global media attention. Now, over three years, according to adjusted Google Scholar data, with 53 citations (34 in Clarivate Analytics' Web of Science), that paper became that author's most cited paper, accounting for one-third of his Google Scholar citations. Design/methodology/approach In this paper, the authors conducted a bibliometric analysis of the authors who cited that paper. Findings We found that out of the 39 English peer-reviewed journal papers, 11 papers (28%) critically assessed Pyne's findings, some of which even refuted those findings. The 2019 citations of the Pyne (2017) paper caused a 43% increase in the Journal of Scholarly Publishing 2019 Journal Impact Factor, which was 0.956, and a 7.7% increase in the 2019 CiteScore. Originality/value The authors are of the opinion that scholars and numerous media that cited the Pyne (2017) paper were unaware of its flawed findings.
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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 | 0.044 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.061 | 0.094 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".