Exploring social vulnerability to earthquakes in the Capital Regional District, British Columbia Canada
Bibliographic record
Abstract
Objective: The primary goal of this research is to identify social vulnerability and resilience to earthquake hazards within the Capital Regional District (CRD) and to generate recommendations for how the provincial health system and various local and regional government agencies can support the continued enhancement of disaster-resilient communities. Methods: Both quantitative and qualitative research methods were employed to evaluate social vulnerability and resilience. Quantitatively, the methodology developed by Cutter et al., was replicated to create a Social Vulnerability Index (SoVI). These data were supported by qualitative data obtained from focus group interviews in three communities in the CRD. Together, this mixed methods approach provided additional insights into the dimensions of social vulnerability, and resilience within the CRD. Results. From the SoVI, twenty-five census tracts (CTs) within the CRD exhibited ‘high social vulnerability’. These highly vulnerable CTs were most likely to be in more densely populated areas, whether they were in inner city neighbourhoods or suburbs of the City. The qualitative results suggest that a large scale seismic hazard will present substantial challenges for the CRD. The smaller, rural and remote communities of Sooke and Port Renfrew appeared to be more interested in emergency preparation than those in the City of Victoria, if judged by their participation rates. Conclusion. The information collected from research participants and the generation of the SoVI complements existing hazard maps and local knowledge well. Both have their place as tools for enhancing understanding of risk-assessment for the area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".