Bibliographic record
Abstract
In recent years, governments and gaming operators across Canada have invested a considerable amount of effort and resources into providing information to their patrons with the explicit or implicit goal of assisting players to make informed decisions about their gambling. The expectation is that better, more complete, information will promote better decisions. Although there is much investment and discussion of informed decision making, the concept itself has received relatively little attention and scrutiny. In 2009, the RGC Centre for the Advancement of Best Practices proposed a review of informed decision-making(IDM) in the gaming sector with emphasis on what information gamblers should have and how best to support their decision making capability. The Review is an in-depth look at informed decision making from the perspective of providing the right information at the right time to gamblers. The research includes: • A review and analysis of literature and materials from the gaming industry (e.g., policy documents, government reports, research), as well as other industries whose products pose risks to their consumers (i.e., Medical Healthcare, Alcohol, Tobacco, Food) • Focus groups with gamblers (see Appendix A) • Interviews with treatment providers (see Appendix B) • The Insight Forum, the 2-day gathering of 35 experts, professionals and other stakeholders to discuss, debate and collect information on issues relevant to informed decision making in 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 | 0.132 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".