Aboriginal street-involved youth experience elevated risk of incarceration
Bibliographic record
Abstract
Objectives—Past research has identified risk factors associated with incarceration among adult Aboriginal populations; however, less is known about incarceration among street-involved Aboriginal youth. Therefore, we undertook this study to longitudinally investigate recent reports of incarceration among a prospective cohort of street-involved youth in Vancouver, Canada. Study Design—Prospective cohort study. Methods—Data were collected from a cohort of street-involved, drug-using youth from September 2005 to May 2013. Multivariate generalized estimating equation analyses were employed to examine the potential relationship between Aboriginal ancestry and recent incarceration. Results—Among our sample of 1050 youth, 248 (24%) reported being of aboriginal ancestry, and 378 (36%) reported being incarcerated in the previous six months at some point during the study period. In multivariate analysis controlling for a range of potential confounders including drug use patterns and other risk factors, Aboriginal ancestry remained significantly associated with recent incarceration (adjusted odds ratio [AOR]=1.44; 95% confidence interval [CI]: 1.12–1.86).Conclusions—Even after adjusting for drug use patterns and other risk factors associated with incarceration, this study found that Aboriginal street-involved youth were still significantly more likely to be incarcerated than their non-Aboriginal peers. Given the established harms associated with incarceration these findings underscore the pressing need for systematic reform including culturally appropriate interventions to prevent Aboriginal youth from becoming involved with the criminal justice system.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".