Mobilizing Resilience : A Disaster Resilience of Place Approach to IFHMP in Squamish British Columbia
Bibliographic record
Abstract
The District Municipality of Squamish faces severe coastal flood risk. However, not all of the 7,477 residents living in the floodplain are created equal in the exposure to these floodwaters. In creating an Integrated Flood Hazard Management Plan (IFHMP) it is critical to address both hazard and vulnerability components of risk. While the strength of floods cannot be controlled, reducing existing vulnerability and maintaining natural services that provide protection provides for long-‐term risk reduction in flood hazard management. Today, the District Municipality of Squamish retains valuable natural flood management assets -‐-‐ such as the Skwelwil’em Squamish Estuary -‐-‐ that delivers an estimated $284 million in flood disturbance regulation services to the District yearly. Meanwhile, the most vulnerable people in Squamish live adjacent to the estuary in the downtown core -‐ the most exposed area to coastal flood events and sea level rise. While the strength of flooding (Hazard) cannot be reduced, the everyday conditions of people and the environment (Vulnerability) can be addressed in local risk-‐based land use planning and community development. Reducing vulnerability has many co-‐benefits, including all hazards planning, poverty reduction, social equity, economic development, ecosystem-‐ based adaptation and integrated resource management. This is the first step to disaster risk reduction and risk-‐based planning in coastal British Columbia. Failing to address these conditions will result in unchanged or increased vulnerability and sensitivity to flood risk. Using the Disaster Resilience of Place approach, this report contains 3 major analyses and illustrates how the local government -‐-‐ in collaboration with the Squamish First Nation and citizens -‐– can effectively build a more flood resilient community and innovate in IFHMP practice. Tools to assist in quantifying vulnerability and resilience and to improve the performance of public engagement are also offered in the Appendix section of this report. Flood Management and Official Community Plans are identified as key platforms to reduce vulnerability with strong ties to local level emergency management planning and long-‐term climate adaptation to coastal flood hazard. These are the primary policy and environmental management pathways identified for implementing a DROP approach in Squamish.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".