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Prohibition, Legalization, and Political Consumerism

2018· reference-entry· en· W2965950731 on OpenAlexaboutno aff
Elizabeth A. Bennett

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsLegalizationConsumerismPoliticsPrinciple of legalityDecriminalizationPolitical scienceCannabisPolitical economyLawSociologyPsychology

Abstract

fetched live from OpenAlex

Cannabis (marijuana) is the most commonly consumed, universally produced, and frequently trafficked psychoactive substance prohibited under international drug control laws. Yet, several countries have recently moved toward legalization. In these places, the legal status of cannabis is complex, especially because illegal markets persist. This chapter explores the ways in which a sector’s legal status interacts with political consumerism. The analysis draws on a case study of political consumerism in the US and Canadian cannabis markets over the past two decades as both countries moved toward legalization. It finds that the goals, tactics, and leadership of political consumerism activities changed as the sector’s legal status shifted. Thus prohibition, semilegalization, and new legality may present special challenges to political consumerism, such as silencing producers, confusing consumers, deterring social movements, and discouraging discourse about ethical issues. The chapter concludes that political consumerism and legal status may have deep import for one another.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.034
Scholarly communication0.0080.003
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.327
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2018
Admission routes1
Has abstractyes

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