Civil-Military Engagement During Public Health Emergencies: A Comparative Analysis of Domestic Responses to COVID 19
Bibliographic record
Abstract
Despite the central role that domestic militaries regularly play in supporting civilian disease outbreak responses, the dynamics of civil-military coordination during major health emergencies remain largely under-explored in public health, humanitarian, and security literatures. Previous research has found, furthermore, that existing international civil-military guidelines hold limited relevance during pandemics, especially at national and local levels, which is currently evidenced by the observable lack of coherence and high variance in domestic military approaches to COVID-19 worldwide. This article presents a comparative analysis of three of these approaches—in the United Kingdom, China, and the Philippines—and maps these countries’ military contributions to the COVID-19 response across a number of domains. Analysis of these case studies builds knowledge and provides important insights into the ways that humanitarian civil-military engagement exists in unacknowledged contexts and forms; how militaries are often ‘first responders’ rather than a ‘last resort’ in crisis contexts; the confusion surrounding how to understand various non-military armed and security actors; and how pandemics represent a unique domain for humanitarian civil-military engagement that tests both the international system and international norms. This paper concludes with policy, guidance development, and research recommendations for improved practice during localised humanitarian civil-military engagement.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".