Challenges in Collaborative Domestic Emergency Management in Canada: Stakeholders' Perspectives on the Role of the Military
Bibliographic record
Abstract
ABSTRACT Research examining the complexities and operational structures of civil–military coordination within large‐scale disaster response frameworks in Canada remains scant. This study addresses this gap by critically evaluating existing civil–military coordination mechanisms, identifying systemic deficiencies, and proposing evidence‐based strategic improvements to effectively manage future major emergencies. To this end, we employ a qualitative approach comprising key informant interviews (KIIs) and a focus group discussion (FGD) with participants from military, federal, provincial, municipal, and nongovernmental organizations to understand the functions and institutional roles of civilian authorities and the Canadian Armed Forces (CAFs) in recent mega‐disasters in Canada. The findings reveal pronounced institutional gaps, including ambiguous delineation of roles and responsibilities, fragmented command structures, and inconsistent communication protocols between civilian and military authorities and stakeholders. These coordination issues are compounded by insufficient joint training and preparedness initiatives, resulting in further operational inefficiencies and delays. To improve collaboration, operational effectiveness, and positive crisis‐response outcomes, stakeholders recommend establishing clear doctrinal guidelines, formalizing interagency coordination structures, and developing comprehensive joint training programs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".