<i>Multiple Barriers: The Multilevel Governance of Homelessness in Canada</i>, by Alison Smith
Bibliographic record
Abstract
Who steps up to respond to new social risks? Which groups and governments engage in building new forms of social protection, which ones stay on the sidelines, and what drives their choices? Alison Smith seeks to answer these questions in Multiple Barriers: The Multilevel Governance of Homelessness in Canada. This impressive book not only fills major gaps in the literature on homelessness but also adds considerably to the bodies of work on federalism and the welfare state. Political scientists have been guilty of the gross neglect of homelessness in Canada, and Alison Smith has gone a long way to remedy this fault. Although the specifics are Canadian, her analysis has implications for the politics of new social risks across contemporary democracies. Multiple Barriers seeks to advance our understanding of governance networks in the policy space of homelessness by exploring “who engages and why.” To answer this question, Smith has marshalled a major empirical base for her study, drawing on documentary sources and close to 100 interviews. The book builds on this mountain of evidence to analyze the roles of not only the federal and provincial governments, but also of four major cities, civic society organizations, Indigenous leaders, and private sector actors in this policy area. It is hard to think of many books on social policy in Canada that have incorporated such a comprehensive cast of characters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".