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Record W4416899792 · doi:10.1002/rhc3.70046

Challenges in Collaborative Domestic Emergency Management in Canada: Stakeholders' Perspectives on the Role of the Military

2025· article· en· W4416899792 on OpenAlexaffabout
C. Emdad Haque, Mohammed Sadman Sakib, Kawser Ahmed

Bibliographic record

VenueRisk Hazards & Crisis in Public Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of WinnipegUniversity of ManitobaResearch Manitoba
Fundersnot available
KeywordsPreparednessFocus groupQualitative researchJoint (building)Key (lock)Emergency managementDisaster responseCommand and control

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.308
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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