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Record W2026357450 · doi:10.1177/002204260303300209

Drug Reform Principles and Policy Debates: Harm Reduction Prospects for Cannabis in Canada

2003· article· en· W2026357450 on OpenAlexaboutno aff
Andrew Hathaway, Patricia G. Erickson

Bibliographic record

VenueJournal of Drug Issues · 2003
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsHarm reductionPunitive damagesCriminalizationEntitlement (fair division)DecriminalizationSanctionsHarmPossession (linguistics)Political scienceIntervention (counseling)CriminologyMandateRhetorical questionLawLaw and economicsPublic healthSociologyPsychologyMedicinePsychiatryEconomics

Abstract

fetched live from OpenAlex

Contrasting the official harm reduction aims of Canada's 10-year national drug strategy with the actual evolution of the Controlled Drugs and Substances Act, the authors find little evidence of harm reduction, and much of sustained and punitive prohibition. The example of the criminal sanctions currently being applied to cannabis possession offences serves to illustrate the limits of what can be achieved in reducing the impact of criminalization when the fundamental ban on personal use and access is retained. Theoretically informed by constructionist analyses of the styles and strategies of social problems discourse, a moral basis of drug use entitlement is expounded from which rational reform might be more fruitfully argued. Despite its official mandate in Canada to develop more pragmatic drug policy alternatives, the harm reduction movement, posing public health solutions based on empirical analysis, is nonetheless needful of a rhetorical foundation by which to denounce prohibition as a morally objectionable intervention in the private lives of individuals.

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.184
Threshold uncertainty score0.946

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0210.021
Scholarly communication0.0170.004
Open science0.0030.004
Research integrity0.0140.010
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 head0.030
GPT teacher head0.334
Teacher spread0.304 · 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 designTheoretical or conceptual
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

Citations65
Published2003
Admission routes1
Has abstractyes

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