Identifying Predatory Journals in Plastic Surgery: A Prospective Study
Bibliographic record
Abstract
Background: Predatory journals promise high acceptance rates and quick publication in exchange for a processing fee. As these journals aim to maximize profits, they neglect traditional mechanisms used to ensure a high-quality publication. Unsolicited email invitations are a characteristic of predatory journals that often inundate the inboxes of surgeons. The objective of this study is to use these emails to identify potentially predatory journals in the area of surgery and plastic surgery. Methods: Unsolicited email requests from surgery-related journals were collected over a 3-month period. Journals were evaluated using a modified version of the Rohrich and Weinstein checklist. The average number of "predatory" criteria met by these potentially predatory journals (PPJs) was compared to that of the top open-access plastic surgery journals which were assumed to be non-predatory for the purposes of this study. Results: In total, 437 unsolicited email requests were received. Of these, 92 emails, representing 57 PPJs, were eligible for inclusion. On average, the PPJs met 5 of the 12 "predatory" criteria, compared to less than 1 in the comparison group. Approximately 96% of these emails, or the respective websites, contained obvious spelling or grammatical mistakes; 98% of these emails came from journals not listed on Scopus, Directory of Open Access Journals (DOAJ), and/or Web of Science. Conclusions: Of the journals that sent unsolicited emails, 98% met 2 or more criteria and were deemed to be predatory. If a journal contains grammatical mistakes and is not listed on Scopus, DOAJ, and/or Web of Science, authors should be cautious.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".