Bibliographic record
Abstract
Defined by researchers as “a silent epidemic” the gambling phenomenon is a social problem that is having negative impact on individuals, families and communities. Among these effects are seen a dismantling of community networks, weakening of family and social ties, psychiatric co-morbidity, suicides and lately more homelessness. Youth, women, elderly, deprived citizens and native communities constitute the social groups that seem to suffer more from gambling accessibility when compared to others. Without pretending to cover all these aspects, we intend, from a social critical perspective, to highlight some of the major psychosocial stakes of the gambling phenomenon. After a brief historical overview underlining the social construction of gambling as a pathology, we will address issues such as the social and ethical contradictions of governments when managing gambling and the heated debate around the disease model of addiction versus a multifactorial approach to this phenomenon. Finally, we propose markers for empowerment while comparing the disease model and the harm reduction one. We hope that these markers can contribute to transfer some power to individuals and their social networks, activate the therapeutic processes and advance the debate on the complex issues that gambling represents in our society.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".