Advancing a Five-Level Typology of Homelessness Prevention
Bibliographic record
Abstract
This paper aims to demonstrate the efficacy of a five-level homelessness prevention typology, encompassing universal, targeted, crisis, emergency, and recovery categories. We argue that this typology can be deployed to illuminate key comparisons in homelessness prevention policy and practice between different jurisdictions and over time. Meanwhile, it avoids the confusions and overlaps that occur in extant categorisations. Using the UK jurisdictions as an empirical testbed for this analytical framework, four key lessons emerge which we contend have resonance across much of the global north. First, though there is growing evidence of the importance of both universal prevention measures (particularly the delivery of affordable housing and poverty reduction), and targeted preventative interventions (focused on high risk groups and transitions), practical action on both fronts has been deeply deficient to date. Second, and more encouragingly, there is a nascent shift in homelessness practice from an overwhelming focus on basic, emergency interventions, towards more upstream attempts to avert the kind of crisis situations that can lead to homelessness arising in the first place. Third, and also welcome, is a trend within recovery interventions from treatment-led to more housing-led models, albeit that this shift has been frustratingly slow to materialise in many countries. Fourth, across all of these categories of homelessness prevention, there remain substantial evidence gaps, especially outside of the US.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".