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Record W7099454653

Clinical Supervisor Addiction Services Vancouver Coastal Health Authority Pacific Spirit Community Health Centre

2002· article· en· W7099454653 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Neural Network Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationLegalizationPublic healthDecriminalizationHarmHarm reductionCannabisPublic policy
DOInot available

Abstract

fetched live from OpenAlex

The opinions expressed in this article are those of the author and are not a reflection of the policies of the Vancouver Coastal Health Authority Reproduced with permission of the Canadian Public Health AssociationAbstract: This article explores, from a public health perspective, the harm done by Canadian drug laws, to both individuals and society. It challenges the perceived dichotomy of legalization and criminalization of intravenous drugs. The article then expands the discussion by exploring eight legal options for illicit drugs and examines how these options interact with; the marginalization of users, the illicit drug black market, and levels of drug consumption. While the main focus of this article is intravenous drugs, it draws some lessons from cannabis research. Résumé: Cet article explore, d'un point de vue de santé publique, le dommage causé aux individus et à la société par les lois canadiennes sur les drogues. Il met en question la dichotomie percue entre la légalisation et la criminalisation des drogues injectées. De plus, l'article explore huit options légales pour les drogues illicites, et examine l'effet que chaque options pourrait avoir sur la marginalisation des utilisateurs, le marché noir de la drogue, et le niveau de consommation de ces drogues. Bien que cet article traite surtout des drogues injectées, certaines de ses conclusions proviennent de la recherche sur le cannabis.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.328
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2002
Admission routes1
Has abstractyes

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