Gender Equality in Gambling Student Funding: A Brief Report
Bibliographic record
Abstract
Acknowledgement of gender disparity in academia has been made in recent years, as have efforts to reduce this inequality. These efforts will be undermined if insufficient numbers of women qualify and are competitive for academic careers. The gender ratio at each graduate degree level has been examined in some studies, with findings suggesting that women’s representation has increased, and in some recent cases, achieved equality. These findings are promising as they could indicate that more women will soon qualify for early-career academic positions. Most of these studies, however, examine a specific—or narrow subset—of academic disciplines. Therefore, it remains unclear if these findings generalize across disciplines. Gambling researchers, and the graduate students they supervise, are a uniquely heterogeneous group representing multiple academic disciplines including health sciences, math, law, psychology, and sociology, among many more. Thus, gambling student researchers are a group who can be examined for gender equality at postgraduate levels, while reducing the impact of discipline specificity evident in previous investigations. The current study examined graduate-level scholarships from one Canadian funding agency (Alberta Gambling Research Institute), awarded from 2009 through 2019, for gender parity independent of academic discipline.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".