The Presence of Predatory and Open Access Journal Publications Among Canadian Plastic Surgery Residency Applicants
Bibliographic record
Abstract
Introduction: As scientific publishing has transitioned online, open access and predatory publishers have surged. This study describes the frequency of publications in potentially predatory and open access journals among applicants to a Canadian plastic surgery residency program, and explores applicant characteristics associated with open access and predatory publishing. Methods: A retrospective review of plastic surgery resident applicants’ curriculum vitae (CVs) from 2015 to 2018 was performed. Published articles listed in CVs were reviewed by 2 authors to identify publication availability, publication year, and publisher. Open access publications were identified using the Directory of Open Access Journals. Predatory publications were identified using Beall's list of potentially predatory publishers. Published applicants’ characteristics were summarized. Applicant characteristics associated with open access and predatory publishing were explored using logistic regression. Results: Of the 186 applicants, 117 published 388 articles and were included in the final analysis. 156 (40.2%) articles were published in open access journals by 76 (40.8%) applicants. 14 (3.6%) articles were published in predatory journals by 14 (7.5%) applicants. Applicant characteristics associated with open access publishing included total number of publications (OR: 1.56, 1.18-1.93, P < .001) and presence of at least one post-baccalaureate degree (OR: 0.36, 0.13-0.95, P = .038). Only an applicant's total number of publications (OR: 1.25, 1.06-1.48, P = .010) was significantly associated with publishing in a predatory journal. Conclusion: These findings stress the importance of raising awareness within the plastic surgery community, including medical students, about the deceptive nature of predatory journals.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".