Finding Mutual Benefit in Urban Development
Bibliographic record
Abstract
Public and nonprofit agencies struggle to compete for space in cities as development pressures and unaffordability intensify. We have identified a potential solution in creative mixed-use projects: ad hoc, cross-sectoral partnerships to develop mixed-use buildings involving a public or nonprofit use. We built our analysis on a census of 54 projects in Toronto (Canada), interviews with 24 stakeholders, and a rich data set of secondary sources. We traced the emergence of this approach in Toronto over 2 decades, mapping its geographical expansion, stakeholder diversification, and the various mutually beneficial spatial arrangements of buildings. Building on the theory of collaborative advantage, we analyzed the motivations behind cross-sector partnered ventures, finding a gradual shift from resorting to partnership in reaction to obstacles to partnerships strategically designed to pool together land, resources, and support for development. Third, we highlight here the role of champions in underwriting risks and the limits of relying on market solutions for social purposes. We conclude by discussing the relevance of collaborative city-building in land-constrained North American planning contexts. Government agencies, nonprofit organizations, and developers alike can benefit from creative mixed-use partnerships, which unlock access to land, resources, development capacities, and community support. Contrary to popular perceptions, intentional separation of nonprofit and for-profit uses can be mutually beneficial. Despite the one-off nature of creative mixed-use development, it can be propelled by an initial cohort of successful partnerships and landmark projects. Limited-time leases, insufficient organizational capacity, and low market demand hinder its implementation.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".