Gender disparity in prestigious speaking roles: A study of 10 years of international conference programming in the field of gambling studies
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this study was to examine the distribution of prestigious speaking roles by gender at gambling studies conferences to better understand the state of gender representation within the field. Keyword searches were conducted in the fall of 2019. A total of 16 conferences that occurred between 2010-2019 and comprising 882 prestigious speaking opportunities were included. Quantitative analysis (i.e., t-tests, chi-squared posthoc tests) was undertaken to evaluate the representation of women speakers and if proportions were the same across genders for speakers. There were significantly less women than men within prestigious speaking roles at gambling studies conferences with only 30.2% of speakers being women (p < .001). This underrepresentation of women was consistent across conference location, speaker continent, speaker role, time, and across the majority of conferences. Women held prestigious speaking roles less frequently than men (M = 1.48 vs. 1.76; p < .001). A 9 to 1 (p < .001) ratio of men to women was found among top 10 most frequent prestigious speakers. While there was a higher proportion of women than men among student speakers and there was no significant gender disparity among early career researchers, there was a significantly lower proportion of women than men among speakers who hold more senior academic positions. There is an issue of gender disparity in prestigious speaking roles at conferences within the gambling studies field. This study highlights the need to counteract gender disparities and make room for diversity within the field.
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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 | 0.000 | 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