Resilience in Suburban Toronto Suburban Renewal in the Face of Future Uncertainties
Bibliographic record
Abstract
This thesis envisions a new suburban approach based on future uncertainties in environmental, economic and social conditions. The review of responses suggest that resilience building is a viable option for such uncertainties and therefore, focus has been placed on Toronto's suburban housing stock, despite criticism for its fragility and inability to function or change in a future without cheap energy. Although it is often argued that low density neighbourhoods will be unsustainable in a future of environmental uncertainty and that they will not endure the coming crises of peak oil and climate change, Toronto's suburban building stock is ideal for resilience building and will in fact be a vital aspect of Toronto's durability in an uncertain future. This thesis examines different aspects of resilience building in regards to environmental, social and economic uncertainty including: localisation over globalisation, economies of well-being, an ecological systems approach, and rethinking zoning regulations and by-laws. This new vision for the suburbs serves not to replace them with dense urban models, but to maintain and add to suburban qualities while also provoking new ideas for introducing resilience into our built environment.
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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 | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".