Social vulnerability to natural hazards in Canada
Bibliographic record
Abstract
While we are exposed to the physical effects of natural hazard processes, certain groups within a community often bear a disproportionate share of the negative consequences when a disaster strikes. This study addresses questions of why some places and population groups in Canada are more vulnerable to natural hazard processes than others, who is most likely to bear the greatest burden of risk within a given community or region, and what are the underlying factors that disproportionally affect the capacities of individuals and groups to withstand, cope with, and recover from the impacts and downstream consequences of a disaster. Our assessment of social vulnerability is based on principles and analytic methods established as part of the Hazards of Place model (Hewitt et al., 1971; Cutter, 1996), and a corresponding framework of indicators derived from demographic information compiled as part of the 2016 national census. Social determinants of hazard threat are evaluated in the context of backbone patterns that are associated with different types of human settlement (i.e., metropolitan, rural, and remote), and more detailed patterns of land use that reflect physical characteristics of the built environment and related functions that support the day-to-day needs of residents and businesses at the community level. Underlying factors that contribute to regional patterns of social vulnerability are evaluated through the lens of family structure and level of community connectedness (social capital); the ability of individuals and groups to take actions on their own to manage the outcomes of unexpected hazard events (autonomy); shelter conditions that will influence the relative degree of household displacement and reliance on emergency services (housing); and the economic means to sustain the requirements of day-to-day living (e.g., shelter, food, water, basic services) during periods of disruption that can affect employment and other sources of income (financial agency). Results of this study build on and contribute to ongoing research and development efforts within Natural Resources Canada (NRCan) to better understand the social and physical determinants of natural hazard risk in support of emergency management and broader dimensions of disaster resilience planning that are undertaken at a community level. Analytic methods and results described in this study are made available as part of an Open Source platform and provide a base of evidence that will be relevant to emergency planners, local authorities and supporting organizations responsible for managing the immediate physical impacts of natural hazard events in Canada, and planners responsible for the integration of disaster resilience principles into the broader context of sustainable land use and community development at the municipal level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".