Disaster Risk in Canada – A Data-Driven Discussion
Bibliographic record
Abstract
In a haphazardly changing climate, decision makers and practitioners need new insights based on historical disasters, demographic and socioeconomic shifts, and modifications in the built environment. The COVID-19 has exposed systemic vulnerabilities at all levels. Reflections on past disasters and practices regarding measures to reduce disaster losses, overlaid with insightful understandings and interpretations to suit current times, must allow for new pathways. This study attempts to achieve just that. It examines natural disasters in Canada since the 1900s as well as census data to track the demographics and socioeconomic scenarios. At the initial assessment, considering the most frequent natural disasters (floods, extreme cold, severe thunderstorms, tropical storms and storm surge, landslides, drought, wildfires, earthquakes, and epidemics), Canada experienced 844 events since 1900. A province-based distribution of these disasters suggests that Ontario is ranked first with 158 major events, followed by Quebec, Alberta, and British Columbia, with over 100 events each. The maritime provinces have also had their share of disasters, and so have the northern communities and territories. In terms of population changes, between 1901 and 2019, Ontario has grown over 560%, Quebec 50%, Alberta 325%, and BC a whopping 2,700%. The study specifically explores the following: disaster types and the scale of their impact on people, properties, and the environment; the demographic and socioeconomic status; an investigation of what measures are currently in place to ensure the building of resilience and coping capacity at the institutional level. The measures include provincial and federal tools for hazard identification and risk assessment that inform policy, emergency response plans, landuse planning, etc. Further investigation is recommended to cover a wide range of vulnerability indicators of the population, as well as the institutional systems and policies in place for developing a robust set of tools to mitigate disaster impacts in the future. This is a preliminary analysis of the entire country in the hope of making a case for a national strategy for disaster adaptive capacities and resilience and climate adaptation.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".