Youth, Crime, and the Potential Cost Offset to Housing First Programs
Bibliographic record
Abstract
Housing First (HF) is an approach that emphasizes providing housing as a precondition for assisting people experiencing homelessness. To the extent that housing reduces contacts with police, HF may reduce criminal behaviour and so reduce costs borne by the justice system. This may be particularly true for youth whose homelessness often forces them to adopt survival behaviour that exposes them to police and bylaw enforcement officers. Using regression analysis, we employ linked administrative data sets from police and from HF programs to examine how interactions of youth with police change following admission to a HF program. An important contribution of our study is the use of administrative police records rather than self-reported data on the number of criminal incidents and their severity. Unconditional quantile regression is used to observe HF’s effect on changes in both the number and severity of criminal incidents. Controlling for demographic characteristics of youth and for type of housing program and using administrative police records as opposed to self-reported police interactions, we find only weak evidence to suggest that the number of criminal incidents falls following admission to a HF program and only weak evidence of a fall in the seriousness of crimes.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".