Between Public Goals and Private Projects: Negotiating Community Benefits for Density from Toronto's Urban Redevelopment
Bibliographic record
Abstract
This dissertation investigates the role of Toronto’s discretionary planning system in extracting public value from density gains in private land development. The three articles that comprise the dissertation focus on three interrelated phenomena: the equity effects of densification and spatial distribution of public benefits; the practice of discretion in planning based on a system of development control and its effects on the delivery of public benefit outcomes; and, different approaches to planning negotiations and the consequences this brings to the governance of public benefits. In policy terms, these interrelated phenomena centre around a discrete area of Toronto’s land use planning framework known as “Section 37” – a section of Ontario’s Planning Act granting exemptions from planning rules for developers in exchange for them providing “community benefits:” parks and open space, public art, affordable housing. Drawing on GIS analysis and 55 semi-structured interviews, the thesis documents the incentive-based mechanism known as density bonusing in Toronto’s land use regime. The analysis highlights the ways in which this planning policy and practice articulates the connections between municipalities and the private development industry. The dissertation argues that density bonusing in planning is a vehicle for examining broader dynamics of urban phenomena, including the institutional and spatial relationship between private development and public good, notions of ‘good planning’, and the role of non-governmental actors in shaping planning processes and outcomes at the neighbourhood scale. More broadly, the dissertation articulates a common observation that can be gleaned from the research findings: the rise of planning tools like density bonusing point to the centrality of zoning and bargaining as a dominant model of planning occurring in cities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".