Developing Community Resilience for Climate Adaptation in Toronto, Ontario
Bibliographic record
Abstract
Climate change is here and will get worse. The City of Toronto must adapt to the forecasted circumstances and prepare its communities to withstand the incoming stresses and shocks. This research paper provides policy recommendations to the City of Toronto that would build the adaptive capacity and community resilience of its neighbourhoods to better withstand the impacts of climate change. It recognizes the value of a hybrid between bottom-up and top-down approaches that includes local residents while aiming toward goals set by the city. It utilizes a literature review of relevant theory and an analysis of Toronto’s existing climate policy to distinguish where the recommendations could contribute value. A case study on Rotterdam, Netherlands, a leader in climate adaptation, and interviews with industry professionals provided insights on policies that the City of Toronto could pursue to improve its adaptation efforts. The two recommendations include the development of neighbourhood resilience plans to increase community adaptive capacity and resilience, as well as a resilience assessment in Toronto’s development review process. Each recommendation would address existing vulnerabilities in communities and would improve access to resources that aid residents during periods of stress and shocks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".