The politics of keeping space: a multi-method study of the housing stability of 'hard to house' persons
Bibliographic record
Abstract
This multi-method study focused on the housing stability of formerly homeless persons who live in two housing programs for 'hard to house' people in Toronto. Specifically, this study answered the following questions: (1) How do "hard to house" tenants who are in the process of being evicted experience and understand their planned evictions? What are their struggles with maintaining housing stability and where do they plan to go if they get evicted? (2) What factors distinguish 'hard to house' tenants in alternative housing who have housing stability from those at risk of being evicted? (3) What resources, programs and policies do the tenants and community housing workers think would increase the housing stability of 'hard to house' tenants in alternative housing? Study methods include long interviews at two points in time with twelve tenants who are in the process of being evicted and two focus groups with fifteen housing workers in the housing programs where the tenants live. A cross sectional survey sampled one hundred and six tenants, fifty-nine with stable housing and forty-seven with unstable housing. The survey questionnaire included standardized measures of quality of life, empowerment, social support, program satisfaction and meaningful activity. One of the central themes from the long interviews was the challenges participants experienced in the shared housing model and the impact of these on participants' well being. Findings from the focus groups illuminated the challenges of working within an empowerment model in a shared housing model. Although analyses of survey data showed no significant differences between the stable and unstable housing groups on demographics and other variables, a multiple logistic regression model identified social support and quality of life (satisfaction with living situation) as significant predictors of housing stability (p (less than) 0.05) when controlling for age, gender, income, race, empowerment and use of community services and support. Findings from the long interviews confirmed those from the survey, deepening and extending our understanding of why those two variables are significant predictors of housing stability. Implications of the findings for policy and practice include the need for more subsidized housing units integrated with the creation of more job opportunities, increased income supports and large-scale efforts to improve health, education and employability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".