Nature-Triggered Disasters and the Involvement of Armed Forces: Exploring a Civil-Military Collaborative Framework
Bibliographic record
Abstract
Nature-triggered disasters have been causing havoc in Canada over the past decade. Although many of these hazards cannot be prevented (e.g., earthquakes), their impacts can be managed through judicious planning and by mobilizing national resources. Considering the relentless force of nature and the degree of anticipation and preparedness needed, Canadian civil and military institutions must synergize to optimally utilize human capital, knowledge, and financial resources. Both the Canadian Armed Forces (CAF) and civil society actors have emphasized the importance of enhancing adaptive capacity and reliance on the armed forces for disaster response. As such, frequent involvement in domestic responses diverts the CAF’s focus away from national and international security threats, underscoring a serious national concern. Against this backdrop, the present paper analyzes existing civil-military cooperative models in Disaster Management in Canada and the USA. Three objectives are set: a) to explore the armed forces’ main tenets and approaches to disaster and emergency management, b) to find similarities and differences in institutional and resource priorities (before and during the onset of extreme nature-triggered events), and c) to identify the best collaborative practices and modes of operation of stakeholders involved. Using a case study approach, a desktop review of policy papers and an event database for two large-scale disasters: one in the United States (Hurricane Katrina in 2005) and one in Canada (the 1997 Red River flood in Manitoba) was carried out. The results offered the following major findings: a) organizational and cultural differences between the civil and military authorities in both countries drive the nature of disaster management; b) centralization vs. resource decentralization has remained the key factor in speeding up disaster response; c) political and legal scope and limitations in civil-military cooperation are often blurred; and d) the sole application of the Command, Control, and Communication (C3) approach becomes problematic when a multi-stakeholder approach is preferred for disaster 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".