Making Markets out of Vice: Gambling, Cannabis, and Processes of State Legitimation and Formation in Canada
Bibliographic record
Abstract
The legalization of gambling and cannabis and the transformation of these practices/substances into consumer markets are processes of state legitimation, naturalization, and (re)formation in Canada. This article examines the moral-cultural transformation of gambling and cannabis over the last 50 years and analyzes these transformations in terms of state-culture dynamics. Where lotteries were legalized in the context of the welfare state, the expansion of gambling beyond lotteries in the 1990s has occurred as the federal state ceded jurisdiction of gambling to the provinces. The consequence has been the direct role of the provinces in the creation of gambling markets. Notwithstanding the monopolization of cannabis by some provinces, the opening of cannabis to private industry (e.g., sales) has occurred relatively quickly. In its central role in market making, the state, paradoxically, appears to disappear. However, the legalization and expansion of gambling and cannabis represent an increased positioning of the state at the nexus of civic and consumer cultures. State formation around consumption of gambling and cannabis centers on state entrepreneurialism and depends on retaining, yet reinventing, notions of harm with a shift from a generalized morality of nation and national spirit to individual risk calculation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".