Gender Equality in Gambling Student Funding: A Brief Report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it