How Do Ideas Get in the Way of Policy Change? A Comparative Study of Homelessness Policy in Toronto and Montréal, Canada
Bibliographic record
Abstract
ABSTRACT While structural factors such as the allocation of resources and responsibilities have traditionally been seen as the key determinants of policy change and stability, the ideas of the people responsible for managing these resources can be just as consequential, especially in value‐laden policy areas such as combating homelessness. Building on the literature on the role of ideas in governance, the analysis of municipal, provincial and federal policy documents, and interviews with 21 municipal and provincial policymakers, community activists, service providers and users, this article compares how Toronto and Montreal implemented the federal government's At Home/Chez soi pilot program informed by housing‐first principles—a marked departure from the previous staircase model in which homeless individuals conditionally moved towards permanent housing based on responsible behavior. Conflicting ideas underpinned understandings of homelessness and intervention priorities in the two cities, while ideas institutionalized in “traditions of governance” were instrumental in Toronto aligning its policies with the federal government's housing‐first experiment and Montreal resisting it.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".