Understanding Disaster Preparedness in Vancouver : Community Perspectives : Summary Report & Annexes
Bibliographic record
Abstract
This report presents findings from a mixed-methods study led by the University of British Columbia’s Disaster Resilience Research Network (DRRN), in collaboration with the City of Vancouver Emergency Management Agency (VEMA). Our research explored disaster preparedness and resilience across Vancouver through surveys and focus group discussions, aiming to better understand how individuals and communities perceive, plan for, and act upon disaster risks in an increasingly complex hazard landscape. The research findings will support work being done by City of Vancouver staff to address barriers to emergency preparedness. Three files are available: the full summary report with annexes (first); the summary report only (second); and the annexes only (third). Acknowledgments: Thank you to Sarah Hunn, Miranda Myles, and Gillian Wong at VEMA; Nicole Paul at UCL; and Stephanie Chang, Kara Gibbs, Carlos Molina Hutt, Ayase Kay, Lara Sarlak, and Jocelyn Stacey at UBC for their contributions to research design, data analysis, implementation, communications, and administration over time. The study was funded in part by the BC Ministry of Emergency Management and Climate Readiness through a contribution agreement with DRRN focused on disaster resilience research, with additional support from UBC’s School of Public Policy and Global Affairs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".