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Record W4386285723 · doi:10.3138/jcs-2022-0033

Making Markets out of Vice: Gambling, Cannabis, and Processes of State Legitimation and Formation in Canada

2023· article· en· W4386285723 on OpenAlexaffvenueabout
James Cosgrave, Patricia Cormack

Bibliographic record

VenueJournal of Canadian Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsSt. Francis Xavier UniversityTrent University
Fundersnot available
KeywordsLegalizationLegitimationCannabisState (computer science)Context (archaeology)Consumption (sociology)JurisdictionPolitical scienceSociologyPolitical economyLawPoliticsGeographySocial sciencePsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.157
GPT teacher head0.408
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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