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Record W3142594333 · doi:10.1177/1469540521990876

“Enjoy your experience”: Symbolic violence and becoming a tasteful state cannabis consumer in Canada

2021· article· en· W3142594333 on OpenAlexaffabout
Patricia Cormack, James Cosgrave

Bibliographic record

VenueJournal of Consumer Culture · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsTrent UniversitySt. Francis Xavier University
Fundersnot available
KeywordsLegalizationSociologyState (computer science)Consumption (sociology)Symbolic powerConsumerismCriminologyLawPoliticsPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This article explores the legalization and marketing of recreational cannabis in Canada, specifically the province of Nova Scotia, that has extended state monopoly over sales. Beginning with Howard Becker’s classic analysis of “becoming a marijuana user,” this ethnographic investigation of the first day of state cannabis sales utilizes and extends Bourdieusian analyses, particularly by showing how “symbolic violence” and “taste distinctions” work beyond overt class reproduction to establish state classifications and rituals. The practices we observe show state formation in action at the point of sale, including education, warning, prohibition, and promotion. As we demonstrate, the state marketing of cannabis works to invite emotional identification toward becoming the state consumer as an embodied habitus. The citizen is not just redeemed morally by the legal recategorization of cannabis but brought into a new subject position as good consumer citizen at the moment of ritual consumption, that is, brought into a “tasteful state.”

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0370.023
Scholarly communication0.0090.002
Open science0.0010.007
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.227
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations9
Published2021
Admission routes2
Has abstractyes

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