Implications for Harm Minimisation in the Management of Problem Gambling: Making Sense of “Responsible Gambling”
Bibliographic record
Abstract
Harm Minimisation and Gambling Harm minimisation has typically been defined as having the goal of reducing the “ adverse health, social and economic consequences of drug (gambling) use without necessarily requiring abstinence … Harm reduction is pragmatic and humanistic, focused on harms and priority issues .” (Centre for Addiction and Mental Health in Canada, cited by Blaszczynski et al., 2001). Harm reduction includes a wide variety of strategies, ranging from public health oriented preventatives through to clinical interventions that focus on low-risk behaviours. The application of the concept to gambling has possibly broadened the range of preventative strategies, which for gambling include consumer complaints mechanisms, codes for responsible marketing, gambling venue staff training, gambling information pamphlets, restricting venue placement of ATMs, design of gaming machine features and venue self-exclusion procedures. Noting that the terms of reference in which any social debate is framed may determine the scope and freedom in which policy debate can develop, Korn et al. (2003) argued that there were benefits from viewing gambling as a public health matter: “ The value of a public health perspective is that it applies different ‘lenses’ for understanding gambling behaviour, analyzing its benefits and costs as well as identifying multilevel strategies and points of intervention .” (p. 236) In this regard, the two national studies released in 1999, one from the USA and the other from Australia, provide a striking illustration of how limiting the debate to a preferred frame of reference or “lens” constricts the policy debate.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".