Bibliographic record
Abstract
Homelessness governance in Toronto is centralized, with the city taking the lead role.While there were periods of innovation and expansion in the city's response to homelessness, that response was also stagnant for several years.Until recently, the city has jealously guarded its power related to homelessness.The Province of Ontario downloaded responsibility for housing policy to municipalities in the 1990s, the only Canadian province to do so.After downloading responsibility, the province re-emerged as an actor in homelessness governance in 2005 and even more so in 2014, with a commitment to ending homelessness by 2025, though municipalities maintain significant responsibility in this effort.Private-and third-sector actors have been involved in homelessness governance for decades.While both have at times exercised great influence in the city, these actors have long struggled to establish institutionalized or long-term roles in the governance of homelessness (though institutionalization is not always the goal of advocacy groups and activists).The involvement of civil society groups in homelessness governance changed in 2014 with the emergence of the Toronto Alliance to End Homelessness (TAEH), comprised of service providers and other stakeholders.The TAEH made significant progress in building and institutionalizing a relationship with the city; the result was a significant shift in local power dynamics so that by 2017, the city and the TAEH were collaborating closely.This evolving role of civil society in Toronto policy is also evident in relation to the federal Homelessness Partnering Strategy.Institutions loom large in explaining the centralized but increasingly inclusive governance dynamics in Toronto.Most obviously, the city has formal jurisdiction over this power.The lack of institutionalized thirdsector involvement in homelessness governance can similarly be understood in part as a result of the political/institutional realities of Toronto; the city covers an enormous territory and has a huge population, so its
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.539 | 0.193 |
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".