Responsible Gambling: A Scoping Review
Bibliographic record
Abstract
Gambling markets have drastically expanded over the past 35 years. Pacing this expansion has been the articulation of a governance framework that largely places responsibility for regulating gambling-related harms upon individuals. This framework, often defined with reference to the concept of responsible gambling (RG), has faced significant criticism, emphasizing public health and consumer protection issues. To study both the articulation and critique of the concept of responsible gambling, we conducted a ‘scoping review’ of the literature (Arksey & O’Malley 2005). Literature was identified through searches on academic databases using a combination of search terms. Articles were independently reviewed by two researchers. Findings indicate 142 publications with a primary focus on responsible gambling, with a high volume of publications coming from the disciplinary backgrounds of the first authors representing the fields of psychology, business, and psychiatric medicine. Further, publication key themes address topics such as responsible gambling tools and interventions, corporate social responsibility and accountability, responsible gambling concepts and descriptions, and to a lesser extent, critiques of responsible gambling. The scoping review of the literature related to responsible gambling suggests the need to foster research conditions to invite more critical and interdisciplinary scholarship in an effort to improve public health and consumer protection.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".