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Record W2531386789 · doi:10.1177/0091450916670393

Perspectives on Cannabis Legalization Among Canadian Recreational Users

2016· article· en· W2531386789 on OpenAlexaffabout
Geraint B. Osborne, Curtis Fogel

Bibliographic record

VenueContemporary Drug Problems · 2016
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsBrock UniversityUniversity of Alberta
Fundersnot available
KeywordsLegalizationCannabisDecriminalizationRecreationCriminologyCriminal justiceEffects of cannabisStigma (botany)Political sciencePsychologyLawPsychiatry

Abstract

fetched live from OpenAlex

This article examines the perspectives of a select group of 41 adult Canadian cannabis users on the decriminalization and legalization of cannabis. The research departs from previous research on cannabis use by focusing on employed adults working in a variety of occupations, including white-collar professionals and graduate students, who use cannabis for nonmedical, recreational purposes. Drawing on in-depth interview data, we explore their perspectives on Canadian law and legal policy and on the possible impact of cannabis reform policies on their own cannabis consumption. Overall, the vast majority of interview participants strongly favored the legalization of cannabis use for the following reasons: (a) prohibition is unjust, (b) economic benefits, (c) reducing violent crime associated with the drug trade, (d) reducing the cost of the criminal justice system, (e) increased safety, and (f) reducing the stigma associated with cannabis use. We conclude by discussing the implications of our research for the literature on cannabis normalization and drug policy reform.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.043
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0240.011
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.266
Teacher spread0.243 · 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

Citations55
Published2016
Admission routes2
Has abstractyes

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