Affective disorders and craving to drugs in heroin addiction
Bibliographic record
Abstract
Due to prohibitionist policies and practices, a poisoned illegal drug supply, and inadequate access to flexible substitution programs, Canada is currently experiencing the worst illegal drug overdose death epidemic in its history. In examining past policies, practices, and discourse that support heroin regulation and drug prohibition, the drivers of the current illegal drug overdose death epidemic in Canada are brought more clearly into focus.This article provides a critical socio-historical analysis of heroin (opioid) regulation with a focus on Canadian federal and provincial policies in the province of B.C., especially the city of Vancouver. Drawing from primary and secondary sources, this article provides a critical socio-historical analysis of heroin (opioid) regulation in Canada.Examining Canada's history of heroin criminalization provides a window to understand the systemic discrimination against people who use illegal heroin and other opioids. From its inception, heroin prohibition has worked to brutally punish a small segment of the population, especially those who are poor, racialized, and gendered. Negative heroin discourse and stereotyping about people who use heroin had an effect, shaping drug law, policing, prisons, and policy and treatment options.Little attention has been given to the increase in heroin possession offences across Canada over nine consecutive years and the lack of heroin substitution programs. Resistance to drug prohibition and criminal approaches to drug use emerged in the 1950s and continue today. Those most affected by drug policies demand inclusion and representation, access to a legal heroin supply, and the establishment and maintenance of heroin buyer clubs, contesting the very foundations of drug control in the twenty-first century.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".