Gambling as a social problem: on the social conditions of gambling in Canada
Bibliographic record
Abstract
Since the 1980s, Canadian legalized gambling has undergone a massive growth, resulting in numerous social problems such as crime, political corruption, and, most importantly, pathological gambling. When it comes to theorizing gambling in Canada, pathological gambling has been the centre of the attention for two related reasons: (1) the increasing concern with individual and social harms resulting from it; and (2) priority given by grant-giving institutions to ‘useful’ scholarly efforts related to it. A major drawback, however, has been that these explanations often overlook the impact of broader social conditions on gambling behaviour and, instead, provide politically and ideologically conservative, microlevel analyses that point to the individual gamblers as the source of the problem. These theories, therefore, present partial accounts of an enduring and growing, socially produced problem. The argument of this paper is that Canadians’ gambling behaviour, and its consequences, must be understood primarily as a social problem and within the nexus of (a) the Canadian state's pro-gambling policies (prompted by the need to generate revenues); (b) the gap between commonly shared Canadian cultural values – monetary success – and legitimate means of achieving them; and (c) capitalist processes of profit-making and commodification. Only then can we have a better understanding of a persistent and growing problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".