Disarming the 'silent killer' : reducing the vulnerability of Toronto's elderly to extreme heat
Bibliographic record
Abstract
Global climate projections forecast more frequent, intense and longer heat waves in the decades ahead. Heat waves are considered the most dangerous extreme weather event for human health, with impacts most pronounced in cities because of the urban heat island effect. Heat-related mortality rates are highest in certain at-risk populations, like the elderly. Toronto, Canada’s largest city, faces two compounding challenges: an increasing number of heat waves coupled with a rapidly growing senior population. This paper, using qualitative interviews, document analysis, and some comparable work on other cities, investigates how response measures in Toronto aim to reduce seniors’ vulnerability to increasing heat waves, evaluates their current effectiveness and explores viable future steps for augmenting these strategies. Using Turner et al.’s 2003 integrated framework, vulnerability is conceptualized as a complex product of both the internal factors of exposure, sensitivity and adaptive capacity within Toronto’s coupled human-environmental system and the external factors beyond this system. Findings highlight how existing city and some volunteer strategies in Toronto work to increase individual and community adaptive capacity, while ongoing city projects aim to reduce exposure levels at a building and municipal level. Failure to reach certain high-risk seniors, the limited success of cooling centres, high temperatures in some aging high-rise apartments and rooming houses, and the city’s over-reliance on air conditioning in light of energy grid instability and municipal environmental objectives are all identified as gaps in current strategies. Promising alternative pathways forward include developing a stronger social infrastructure with more securely funded community networks to support the elderly, better housing through targeted retrofitting of high-risk properties, and transitioning away from air conditioning dependence through more passive cooling design and expansion of the city’s use of deep water cooling. Findings about the Toronto situation also have applicability to other similar Canadian cities in the provinces of Quebec and Ontario, and in the United States.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".