‘Nepotistic journals’: a survey of biomedical journals
Bibliographic record
Abstract
Abstract Context Convergent analyses in different disciplines support the use of the Percentage of Papers by the Most Prolific author (PPMP) as a red flag to identify journals that can be suspected of questionable editorial practices. We examined whether this index, complemented by the Gini index, could be useful for identifying cases of potential editorial bias, using a large sample of biomedical journals. Methods We extracted metadata for all biomedical journals referenced in the National Library of Medicine, with any attributed Broad Subject Terms, and at least 50 authored (i.e. by at least one author) articles between 2015 and 2019, identifying the most prolific author (i.e. the person who signed the most papers in each particular journal). We calculated the PPMP and the 2015-2019 Gini index for the distribution of articles across authors. When the relevant information was reported, we also computed the median publication lag (time between submission and acceptance) for articles authored by any of the most prolific authors and that for articles not authored by prolific authors. For outlier journals, defined as a PPMP or Gini index above the 95th percentile of their respective distributions, a random sample of 100 journals was selected and described in relation to status on the editorial board for the most prolific author. Results 5 468 journals that published 4 986 335 papers between 2015 and 2019 were analysed. The PPMP 95 th percentile was 10.6% (median 2.9%). The Gini index 95 th percentile was 0.355 (median 0.183). Correlation between the two indices was 0.35 (95CI 0.33 to 0.37). Information on publication lag was available for 2 743 journals. We found that 277 journals (10.2%) had a median time lag to publication for articles by the most prolific author(s) that was shorter than 3 weeks, versus 51 (1.9%) journals with articles not authored by prolific author(s). Among the random sample of outlier journals, 98 provided information about their editorial board. Among these 98, the most prolific author was part of the editorial board in 60 cases (61%), among whom 25 (26% of the 98) were editors-in-chief. Discussion In most journals publications are distributed across a large number of authors. Our results reveal a subset of journals where a few authors, often members of the editorial board, were responsible for a disproportionate number of publications. The papers by these authors were more likely to be accepted for publication within 3 weeks of their submission. To enhance trust in their practices, journals need to be transparent about their editorial and peer review practices.
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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 | 0.011 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".