Vulnerability and Adaptive Capacity to Heat Waves in Fast-Growing, Mid-Sized Cities: Guelph Case Study
Bibliographic record
Abstract
As climate change continues to worsen, and urbanization creates more grey infrastructure, heat waves will continue to become more severe and frequent. The impacts of heat waves are not consistent spatially, and certain groups are more affected than others. The way municipal climate plans address the issue of heat waves is critical in the city’s efforts to protect residents. Guelph, a rapidly growing mid-sized city in Ontario, Canada, is used as a case study. This thesis examines how vulnerable populations are distributed, and how municipal climate plans invoke heat waves, address vulnerability, and whether vulnerable populations are incorporated in the planning framework. Further, the policy framework is assessed for its focus on exposure versus sensitivity, as well as mitigation versus adaptation. The findings indicate that there are clear areas in the city that have higher degrees of vulnerability based on sensitivity characteristics. In addition, the planning framework makes little indication of efforts to address vulnerable populations or heat waves, and takes a more exposure-mitigation approach to environmental hazards. The current setup of the planning framework can be improved to include a sensitivity-adaptation approach by adjusting the framing of various mitigation strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".