Housing First the conversation: discourse, policy and the limits of the possible
Bibliographic record
Abstract
Researchers, policy-makers, and political leaders in Canada and the US are championing the ‘Housing First’ (HF) intervention as a solution to homelessness. HF supplies people experiencing both homelessness and challenges around mental health with housing and a range of supports that can include case-coordination, psychiatry, and primary care. While HF’s impact on the housing status of individual participants has received considerable scientific and public consideration, less attention has been paid to its effects on societal conversations related to housing, public services, and social justice. We explore some of the impacts, not of HF the intervention, but of HF the conversation – the way public documents related to HF interact with broader discourses. Specifically, we examine the potential for this conversation to undermine the ultimate goal of ending homelessness in Canada. We conclude that positioning program interventions – no matter how important in the current context – as singular solutions to issues like homelessness or preventable chronic disease risks obscuring distal causes and marginalizing systemic responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.057 | 0.091 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".