Lessons from the Garden City Movement: Making a Case to Regionalize City of Toronto’s Tower Renewal Program
Bibliographic record
Abstract
The urban landscape spatially articulates diverse urban realities and historical trajectories of urban development, ideals and visions, and governance structures. Urban thinkers have often proposed utopian ideals of cities, which would alleviate the social issues of the society they were living in. Ebenezer Howard and his Garden City vision is a sustainable city utopia that offered an alternative to the industrial capitalist city. On the other hand, Modernist urban thinkers such as Frank Lloyd Wright and Le Corbusier championed car-oriented cities. The ‘tower in the park’ vertical city of Le Corbusier has deeply impacted the urban landscape of Toronto, and now more than 50 years later, these towers provide the largest proportion of affordable housing stock for low-income groups and newcomers. This paper analyzes the place-based Tower Renewal program of the City of Toronto, which sets to ameliorate the physical and social disparities that are disproportionately concentrated in tower communities. This paper proposes the 15-minute city as a 21st-century approach to the Garden City for the tower communities of Toronto, which helps to connect this place-specific initiative to broader economic and urban restructuring trends in the region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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".