Socially responsible and accountable gambling in the public interest
Bibliographic record
Abstract
While much has been written about the need for governments and the gambling industry to act responsibly in their provision of gambling, only modest advances have been made to establish best practices in this area. Worldwide, few governments even approach what William Eadington, in Trends in gambling and responsible gaming in the US and elsewhere (2003, http://www.888betsoff.com/links/04_presentations/Eadington.pdf), calls a stage-four level of responsible gambling stewardship, that is, the unconditional acceptance of strong measures to attenuate gambling-related harms. One of the cornerstones of a gambling regime oriented toward consumer safety and public interest is a commitment by government and the gambling industry to meet commendable standards for accountability and social responsibility. After studying the government's legislative framework for the operation and regulation of gambling in the province of Ontario (Canada), reviewing the province's gambling-related mission and public-policy statements, and interviewing key actors in the government's gambling administration, a template was developed for an optimally socially responsible and accountable gambling regime that operates in the public interest. The template, along with suggestions for improving accountability and social responsibility in the provision of gambling, is presented.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".