Gender gaps in publications and citations in gambling studies: Comparisons against addiction science.
Bibliographic record
Abstract
OBJECTIVE: Women in academia publish fewer papers and receive fewer citations than men. These gender gaps likely reflect systemic biases operating over several levels, from journal editorial policies to academic career progression. This study sought to characterize gender gaps for publications and citations in the field of gambling studies. METHOD: An automated gender inference procedure classified authors' binarized gender from their first names. Gender gaps were computed for publications and citations of papers in gambling studies, using the wider field of addiction science as a benchmark. Publication data were scraped from eight peer-reviewed gambling/addictions journals and separately from all gambling publications listed in PubMed. RESULTS: Men authored 16% more publications than women among gambling papers and 23% more publications among nongambling addictions papers. Although robust gender gaps were observed in specialist gambling journals, we find limited overall evidence for gender inequality being greater in gambling studies. Indeed, among nongambling addiction papers, men published more, despite a greater apparent representation of women in the field. The gender gap was most pronounced for the last authorships, denoting seniority. Among the first authorships, there was variability between journals, and some journals displayed approximate parity. There was limited evidence for any corresponding gender gap in citation counts. CONCLUSIONS: Gender gaps in gambling research, and addiction science more broadly, adhere to wider trends in academia, including the associations with academic seniority. Variability between individual journals supports the role of journal editorial policies to increase the representation and visibility of women researchers in addiction science. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".