Pathways to criminalization for street-involved youth who use illicit substances
Bibliographic record
Abstract
Illicit drug use and homelessness among street-involved young people remain community and public health concerns, in part because of their association with ‘public disorder’, as well as increased encounters between youth, police, the criminal justice system, and the associated health-related harms. In the public imagination, illicit drug use, homelessness, and police encounters (including incarceration) are often understood as problems rooted in individual biographies. In general, there has been a lack of attention to the larger historical, institutional, and social-spatial contexts that converge across time, to increase young people’s risk of coming into contact with police and the criminal justice system. Drawing from a longitudinal ethnography with street-involved young people who use illicit drugs in Vancouver, Canada, we highlight two qualitative case studies that illustrate some of the ‘pathways’ to criminalization among this population. Specifically, these case studies reflect the complex linkages between child apprehension, foster care, homelessness, illicit substance use, and incarceration (juvenile detention and prison) across time. Our findings highlight the role of state interventions in perpetuating the marginalization that occurs across young people’s lives, in ways that increase their vulnerability to police and criminal justice encounters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".