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.
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 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 | 0.005 | 0.387 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".