The Promises of Housing First and the Realities of Neoliberalism: Lessons from Toronto's Streets to Homes Programme
Bibliographic record
Abstract
In 2005, Toronto City Council adopted the Streets to Homes programme, based on the “Housing First” (HF) approach, as the centrepiece of the City’s strategy to address homelessness (City of Toronto, 2005). Under HF, a client is placed in permanent housing immediately, and then receives supports for concurrent issues, such as addiction and mental illness, that affect housing stability (Hwang et al, 2012). HF is being widely embraced by North American policymakers and academics, who applaud its utility in facilitating permanent housing retention (Waegemakers Schiff and Rook, 2012). However, critics argue that HF programmes facilitate the removal of marginalised populations from city centres and represents a retrenchment of front line emergency services, reflecting neoliberal governance and the regulation of space (Klowdasky, 2009; Willse, 2010). Both the heavy reliance of quantitative, medically-oriented measures employed by proponents of HF and the equally abstract arguments of its detractors fail to assess the implications of a city’s embrace of HF on the overall housing and homelessness policies and the regulation of space. The current study intends to overcome these gaps by presenting qualitative research conducted through interviews with City of Toronto officials, service providers, and other key informants. The initial research questions focused on why the decision to adopt Streets to Homes was made, and its impacts on service delivery and access to public space. However, the employ of grounded theory allowed for a more holistic understanding to emerge of how HF does not represent neoliberalism, but rather is hampered by it. In order for HF programmes to succeed, they must be supported by a robust supply of affordable housing and adequate income supports, as well as a great deal of attention being paid to the psychosocial issues that often accompany homelessness. A failure to have these supports in place results in continued extreme poverty and poor community integration for clients.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.038 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".