Responsibilities in gambling harm prevention and reduction
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
Gambling harm prevention and reduction consists of a range of upstream and downstream solutions. Responsibilities for implementing and ensuring these tasks falls across a range of actors, including policymakers, regulators, health professionals and industry. Increased harms caused by online gambling necessitate new regulatory measures, and potentially new responsibilities for their implementation. The current study uses key informant interview data (N=10) conducted in four jurisdictions that have recently introduced a license-based online gambling market (Germany, the Netherlands, Sweden, Ontario). Our aim was to identify what kind of responsibilities for harm prevention and reduction emerge in competitive online markets, to whom responsibility for these tasks is assigned, and what kind of barriers to harm prevention exist across responsibilities. Our analysis shows that most universal responsibilities are assigned to policy makers and regulators. Selective measures aiming at those who gamble, are largely implemented in collaboration between regulators and industry. Indicated and treatment-focused measures are the shared responsibility of treatment professionals, regulators and industry. The main barriers to effective harm prevention related to conflicting interests, industry power, lacking harm prevention resources, lacking centralisation and offshore provision. We argue that improved harm prevention would require balancing existing asymmetries that relate to power, responsibilities and prioritisations.
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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 | 0.001 |
| 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