Mapping the Conceptualization of Gender in Gambling Literature
Bibliographic record
Abstract
This scoping review aims to map the existing conceptualization of gender in peer-reviewed gambling scholarship to locate areas of future inquiry for a comprehensive understanding of the relationship between gender and gambling. It follows Arksey and O'Malley's (2005) framework for scoping reviews, updated by Levac et al. (2010) and Daudt et al. (2013). We located the relevant literature published between 2000-2020 by searching through eight academic databases using Boolean operators and various key search terms, yielding 31,533 results. After a thorough screening based on inclusion/exclusion criteria and excluding duplicates, we located 2,532 journal publications that addressed gender and gambling. Among them, 53.4% used gender as a descriptive demographic variable, 44.3% explored the comparative analysis between men’s and women's gambling behaviors, preferences, and risks, and only 2.3% focused on gender from a socio-cultural perspective. When articles mentioned gender, we found that it was primarily considered a descriptive demographic variable and an indicator of comparative analysis between men and women. Furthermore, the few articles that discussed the socio-cultural aspects of gender were mainly limited to a binary construction of gender. This scoping review concluded that there is a scarcity of socio-cultural studies of gender in gambling scholarship, indicating the need to expand socio-cultural analysis in research on gender and gambling.
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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 | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.043 | 0.030 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".