Planning for disaster resilience in rural, remote, and coastal communities: Moving from thought to action
Bibliographic record
Abstract
Disaster resilience is the cornerstone of effective emergency management across all phases of a disaster from preparedness through response and recovery. To support community resilience planning in the Rural Disaster Resilience Project (RDRP) Planning Framework, a print-based version of the guide book and a suite of resilience planning tools were field tested in three communities representing different regions and geographies within Canada. The results provide a cross-case study analysis from which lessons learned can be extracted. The authors demonstrate that by encouraging resilience thinking and proactive planning even very small rural communities can harness their inherent strengths and resources to enhance their own disaster resilience, as undertaking the resilience planning process was as important as the outcomes.The resilience enhancement planning process must be flexible enough to allow each community to act independently to meet their own needs. The field sites demonstrate that any motivated group of individuals, representing a neighborhood or some larger area could undertake a resilience initiative, especially with the assistance of a bridging organization or tool such as the RDRP Planning Framework.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".