Clinical Supervisor Addiction Services Vancouver Coastal Health Authority Pacific Spirit Community Health Centre
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.328 | 0.041 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".