Resilient Recovery: A systems analysis of disaster recovery in Canada
Bibliographic record
Abstract
The frequency and severity of disasters caused by natural hazards and extreme weather is increasing across Canada. Each year, more communities face devastation, disruption, and the difficult task of rebuilding. However, the process by which communities pick up the pieces – disaster recovery – is currently failing to deliver more resilient communities. In Canada, disaster recovery prioritizes the rapid return to pre-disaster conditions without consideration for the changing risk environment and the ways in which recovery can enable communities to better prepare for the future. \nOur commitment to this failing system has long-term consequences. With the cost of disasters dramatically increasing, how we rebuild communities contributes to their vulnerability or resilience in the future. Because of the static nature of physical structures, with building and infrastructure lifespans of more than fifty years, recovery locks in the risk profile of a community’s built environment for generations. \nThis project examines the barriers and opportunities for municipalities, the level of government closest to the individuals and businesses devastated by disaster, to integrate systematic disaster risk reduction into recovery and thereby rebuild more resilient communities. Using systems thinking informed by foresight and human-centered design research methods, this study aims to identify the constraints and leverage points for changing our approach to recovery in Canada so it prioritizes resilience to future risks instead of recreating the past. \nResilient Recovery: A systems analysis begins by describing disaster trends in Canada and the factors increasing disaster risk, then traces of evolution of the disaster recovery system and analyzes the dynamics at play in the current system. It explores emerging forces of change and the implications these emergent issues may have for recovery, then concludes with an analysis of the system’s leverage points, considerations for how foresight could enhance the process, and a proposed pathway towards transformational change.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".