Finding Mutual Benefit in Urban Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Problem, research strategy, and findings 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.Takeaway for practice 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it