Learning from the Drug Policy Curriculum: People Who Use Drugs Policing in the Context of Decriminalization
Bibliographic record
Abstract
Police officers have long been tasked with translating drug policies into practice; as a key public-facing side of the criminal justice system, they influence how drug policy messages are conveyed to the public through everyday enforcement practices. The government of British Columbia, Canada, received a 3-year exemption from federal drug laws to decriminalize the possession of small amounts of most illicit substances starting January 31, 2023. In this context, we explored what people who use drugs learned from drug policy as it was taken up into policing practice. We use constructs from curriculum theory as a framework to understand what policing explicitly and implicitly communicates to people who use drugs. We analyzed 40 qualitative interviews with people who use drugs in socioeconomically stable positions (housed and employed) in the first year of decriminalization in British Columbia to understand lessons gleaned from policy and policing in this policy context. Findings show that the formal curriculum of drug policy provided a sense of relief for many participants who could ease their fears of being labelled "criminals." However, the way that drug policies were applied by officers in practice, making explicit a hidden curriculum, shaped how participants saw themselves and other people who use drugs in ways that were stigmatizing. Our research shows the value of analyzing the hidden curriculum of drug policy to illuminate how it shapes the way in which people who use drugs construct and position themselves.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".