Developing a research-specific emergency management programme for municipal resilience following the 2013 flood in southern Alberta
Bibliographic record
Abstract
Calgary was significantly impacted by the southern Alberta floods in 2013. Prior to the 2016 Fort McMurray wildfires (also in Alberta), these floods were the costliest disaster in Canadian history. In the aftermath, the Conference Board of Canada's independent review of the overall performance of Calgary's Recovery Operations Centre (ROC) recommended the need to: (1) include community actors and external stakeholders; and (2) develop pre-event situational awareness for those citizens who lack the ability to request assistance through regular channels. In response to these findings and a seemingly consistent experience of emergencies and disasters, the Calgary Emergency Management Agency (CEMA) sought to develop a comprehensive all-hazards emergency management programme for community leaders and citizens to further enhance municipal resilience when faced with inevitable future challenges. Using a case study approach, this paper presents the background, methodology and realisation of this programme, offers recommendations for challenges and limitations, and considers the key impetus for its development - the prevalent yet complex concept of resilience in disaster and emergency management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".