A New Direction: A Framework for Homelessness Prevention
Bibliographic record
Abstract
Prevention makes sense. To prevent disease, we vaccinate. To prevent traffic deaths, we install seat belts. While we recognize intuitively that preventing homelessness is a good idea, there has been little movement in Canada to make that happen on a national scale. A New Direction: A Framework for Homelessness Prevention sets out to provide the language and clarity to begin that conversation. \n \nSince mass homelessness emerged in the mid-1980s, we have largely used emergency services to respond to people’s immediate needs. While we will always need emergency services to help those in crisis, over time these short-term responses have become the standard method for managing homelessness long-term. In the last decade, Canadian policies and practices have begun to shift from managing homelessness to finding solutions, in particular the expansion of the Housing First approach across the country. The Housing First model provides housing and supports for people experiencing chronic homelessness with no housing readiness requirements. New research, innovation, and best practices have propelled our thinking to make the goal of ending homelessness realistic; however, we are still missing an important piece – preventing homelessness in the first place. Why must we wait until people are entrenched in homelessness before offering help? \n \nIn A New Direction: A Framework for Homelessness Prevention, we set out to uncover what it will take to stop homelessness before it starts, to avoid its often-traumatizing effects. The aim of the framework is to begin a nation-wide conversation on what prevention looks like, and what it will take to shift toward homelessness prevention. Using international examples, the framework operationalizes the policies and practices necessary to successfully prevent homelessness and highlights who is responsible. Above all, it situates prevention within a human rights approach. Now is the time to prioritize homelessness prevention.
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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 | 0.030 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".