Bibliographic record
Abstract
In recent years, discourses of responsibility have, along with the proliferation of gambling and problem gambling itself, become increasingly prevalent throughout Western nations. The concept, which originates in notions of power and morality, has been appropriated by a range of stakeholders who utilize it in particular ways. In a pragmatic sense, ideas about responsibility are often generated and fostered through strategic alliances between, for example, government, the gambling industry, community groups, and treatment providers whose interests can be made to coalesce around this central theme. For instance, governments have moved to formulate responsible gambling policies, while some sectors of the gambling industry have attempted to demonstrate their commitment to social responsibility by, for example, not encouraging excessive play and providing realistic estimates of the chances of losing (or at least by paying lip service to those principles). At the same time, treatment agencies provide advice and information that is designed to encourage the development of responsible, self-regulating behaviour in their clients. Meanwhile, a range of organizations have come to identify themselves in terms of this increasingly dominant discourse, including, for example, the Responsible Gambling Council in Canada, the National Center for Responsible Gaming in the USA, and the Responsibility in Gambling Trust in the UK.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.086 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.034 | 0.059 |
| Insufficient payload (model declined to judge) | 0.009 | 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".