People, Place & Landscape : A Bottom-Up, Adaptive, Catalytic Approach to Tower Renewal
Bibliographic record
Abstract
The process of improving poor, declining urban neighbourhoods is essential for the health and well-being of individuals as well as the prosperity of cities and nations. Despite the clear practical and ideological reasons for doing this, throughout history, governments and planners have struggled to find workable solutions. Today, it is becoming increasingly clear that in order to achieve equitable, substantive and sustainable improvements in poor urban neighbourhoods, the solutions must be layered and account for the interrelatedness of social, economic, and physical realms. Given the complexity of this process, this research suggests that bottom-up, adaptive and catalytic approaches to urban renewal can help planners to achieve substantive and sustainable change. Further, as contemporary urban theory suggests, the notions of landscape and place are uniquely well-suited mediums for supporting and producing change in a complex world. The Mayor's Tower Renewal Project in the City of Toronto, is an urban renewal initiative that demonstrates both the importance and complexity of urban renewal. As such, it provides an opportunity to understand how bottom-up, adaptive, and catalytic approaches which engage the urban landscape can result in significant improvements to the conditions of a declining urban area. Based on this analysis this research paper offers a new lens for thinking about and reacting to the process of urban revitalization in a way that produces equitable, long-lasting and meaningful change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".