Overview of Military Operations in Response to Domestic Emergencies, Global Pandemics, and Civil-Military Relations
Bibliographic record
Abstract
Abstract Domestic military operations in democracies had historically been rare, reserved for extreme emergencies. However, the proliferation of unconventional threats, notably climate change, biosecurity hazards, and terrorism, has significantly increased the frequency, complexity, and duration of domestic deployments. This book provides a global comparative study of domestic military operations, with a specific focus on the COVID-19 pandemic. The diverse 26-country contributions investigate how varying national contexts, governmental systems, and military structures influenced these deployments, as well as the impact on civil-military relations. While the pandemic created similar demands, states responded differently, allowing for the identification of broader patterns. The research examines the legal frameworks for domestic operations, the specific roles militaries played during COVID-19 (e.g., medical support, logistics, security), and implications for civil-military relations. The case studies show that due to their readiness and organizational capacity, militaries across the globe are increasingly called on domestically as a “force of last resort.” However, extensive deployments and so-called “mission creep” are straining resources for force generation and sustainment, affecting operational readiness, especially for core combat missions. This trend challenges traditional conceptions of civil-military relations theories, raising concerns about blurred roles and professionalism. The volume draws on principal-agent theory for a more nuanced understanding of civil-military dynamics during the pandemic: political executives used the military as an agent, with heightened risk for "agency slack" or "securitization," depending on civilian oversight. The volume concludes that the armed forces need to be versatile, agile, and adaptable as "guardians of the nation" in response to the evolving security environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".