Making the Prevention of Homelessness a Priority: The Role of Social Innovation
Bibliographic record
Abstract
Abstract Mass homelessness emerged in Canada in the wake of neoliberal policies that reduced government production of housing and other supportive measures. Efforts to reduce homelessness have occurred in three stages: 1) an emergency response in the 1990s that consisted mostly of investment in shelters, soup kitchens, and day programs, 2) the implementation of community plans to end homelessness, combined with the adoption of Housing First as a strategy that seeks to provide reliable shelter as a first step to anyone without it, followed by other remedial services, and 3) the recent development in Canada of early intervention strategies to prevent homelessness from its inception. The second stage was highly successful in dealing with the situation of chronically homeless adults, and many communities have begun to see reductions in homelessness. However effective, this approach does not break the cycle by intercepting potentially homeless individuals in their youth, which is when it begins for many people. Canada is at the beginning stages of the move towards a stronger focus on prevention, aided by a social innovation agenda to identify, design, test, and evaluate preventive interventions to determine which ones will be most strategically effective, setting the stage for implementation and going to scale.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".