Balancing Supportive Housing with Civic Engagement Balancing Supportive Housing with Civic Engagement
Bibliographic record
Abstract
The opinions expressed in this or any paper published by the Centre for Urban and Community Studies do not necessarily reflect the views of the Centre, or those of the University of Toronto. Centre for Urban and Community Studies • University of Toronto • www.urbancentre.utoronto.caBalancing Supportive Housing with Civic Engagement iii Executive Summary “Ugly, ” “terrible, ” “offensive”: these are some of the many words used by citizens, housing agencies, and city staff to describe public meetings involving the development of supportive housing for psychiatric survivors. The lengthy and heated conflicts that arise during consultation present challenges that must be addressed, such as discrimination against psychiatric survivors and the expense of Ontario Municipal Board hearings. This report examines Toronto’s consultation practices to identify what contributes to conflicts between community members and housing providers, and suggests changes to the planning process to help overcome these conflicts and encourage the development of supportive housing. Two of Toronto’s current priorities are to develop more affordable and supportive housing, and to improve citizen engagement in the planning process. Unfortunately, these two priorities often
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".