Time to Shuffle the Deck
Bibliographic record
Abstract
The underrepresentation of women speakers at academic conferences in the gambling studies field is historic and pervasive yet the pressing issue of gender disparity in the field has yet to be adequately acknowledged or addressed. In 2023, an International Forum, hosted by Research And Networking for Gambling Early-career Scholars (RANGES) brought together ten early career researchers in the gambling studies field, all women, from varied research environments and disciplinary backgrounds. The International Forum aimed to answer the question: How can gambling studies conferences improve gender representation and become more inclusive and equitable? The result is this commentary which constitutes a set of recommendations related to gambling studies conferences designed to be implemented broadly as a form of collective action that will (1) contribute to the development of a shared understanding and awareness of the pressing issue of gender disparity; (2) provide actionable pathways for change; and (3) trigger a systemic and comprehensive change in the face of ongoing gender disparity with the goal of creating a more equitable, diverse and inclusive research field.
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.
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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.168 | 0.081 |
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".