‘Housing First’ and the Changing Terrains of Homeless Governance
Bibliographic record
Abstract
Abstract Over the last fifteen years, programs based on ‘housing first’ models have swept to prominence as solutions to homelessness. Such programs serve a small subset of the overall homeless population, namely the ‘chronically’ homeless, offering direct access to permanent housing with comprehensive and flexible support services attached. Hailed as socially progressive responses to homelessness—based on their opposition to traditional emphases on client passivity, sobriety and moralised deservingness—the popularity of housing first models has often depended on congruence with wider projects of welfare retrenchment and fiscal austerity. Despite the rapid globalisation and high public profile of housing first ideas, they have been largely overlooked in geographical accounts of homeless governance. In response, this article discusses the growing importance and influence of housing first ideas, before looking to contemporary debates on homeless governance for interpretive insights. Informed by these debates, we sketch conceptual areas to which future research on housing first models and programs might attend: first, to their ambivalent politics and, second, to the processes and practices of translation that are central to their implementation and political consequence. Moving beyond questions of operational efficacy, efficiency and fidelity, we call for critical but constructive accounts focused on the constitutive relations between housing first ideas and governance transformations at and across a range of scales and sites.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.081 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".