Bibliometric analysis of a controversial paper on predatory publishing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.028 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.681 | 0.931 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it