Publish and/or perish: A urological perspective on predatory publications
Bibliographic record
Abstract
INTRODUCTION: It is an accepted axiom that academics must publish to be considered successful. Open-source journals are quickly gaining traction in the scientific community as an effective way to disseminate important research. The open-access movement includes many successful, well-respected operations, but has also spawned a plethora of journals, some predatory and others that appear to be amateurish academic traps. We provide a first look at open-source journals, both reputable and predatory, specifically pertaining to urology. METHODS: A review of the email inbox of a single academic urologist was examined for journal article solicitations over a four-month span. Journals were excluded if they did not pertain to urology. Journals were analyzed according to journal-centred metrics (H-index, number of documents published, total citations, and number of citations per document) over one publishing year (2015). RESULTS: A total of 32 journals contacting a single academic urologist were included in this review. The majority of journals originated from North America (84.3%) with a mean cost of $1567 CAD. Of the 32 journals, only seven were listed on reputable databases. Of these journals, analysis of journal-specific metrics showed, on average, a journal H-index of 6.71, total documents published over one year of 66.14, and number of citations per document of 0.59. Some publications were found to make false claims of listing in vetted academic databases. CONCLUSIONS: Choices for open-source journal publication are rapidly increasing in the field of urology. They are not all created equal. Publication in many of these journals will increase the risk of seeing academic careers perish rather than flourish.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".