Retrofitting Tarmac Wastelands: Designing for Social and Ecological Permeability
Bibliographic record
Abstract
This thesis investigates urban strategies to restore ecological and social permeability in underutilized tarmac surfaces of commercial and industrial areas built on ecologically sensitive sites. The city of Toronto has seen urban development where the natural flow of many creeks and ravines had been dramatically altered and re-engineered to flow through concrete culverts buried beneath the vast fields of impervious asphalt surfaces (parking lots), commercial complexes, industrial or residential development. One such site is the Leaside Industrial and Business area that sits on the path of the buried Walmsley brook, a part of the Don River watershed. This site is surrounded by the Don Valley on three sides has been facing major ecological challenges like flooding due to the presence of vast areas of impervious surfaces. The area also faces social challenges, like a disconnect between the Leaside and Thorncliffe Park Neighborhoods and the gradual loss of affordable housing. The site has a significant industrial past and, over the years, saw retail encroachment with the arrival of SmartCenters and other big boxes. With the Eglinton LRT coming up and the development pressure, many industrial companies are migrating to suburbs, giving rise to underutilized industrial and commercial spaces. The site is ripe for transformation and has the potential to explore alternate ways for redevelopment. \nThe design strategies that would be used to arrive at an urban retrofit proposal would address the aspects of permeability at different scales— from surface permeability, designing for flood resilience, reviving parts of the buried hydrology, and parallelly strengthening the social aspects. This would also provide an opportunity to incorporate sites for local food production in community agriculture and local cafes to increase food security and catalyze social permeability between the two neighborhoods. The design proposal would put forward an alternate way of redeveloping post-industrial sites on buried creeks in Toronto. The key impact of this research would be to question the present redevelopment practices and explore a more ecologically and socially sensitive approach, while examining the transformative potential of these vast under-utilized surfaces.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".