The role of science in resilience planning for military-civilian domains in the U.S. and NATO
Bibliographic record
Abstract
In recent years, the NATO member nations have committed to a coordinated approach to strengthening resilience among the Allies, including the development of National Resilience Plans (NRPs). The Allies outlined the extent to which the robustness of their respective military capacities requires the designed resilience of systems that bridge civilian and military domains. This article outlines the role that resilience plays in supporting tactical and strategic measures of national security and defense within military and civilian domains. This exploration provides an outline of how resilience is currently applied in practice by the U.S. Department of Defense (DOD) and NATO. Building on this diversity of applications, various categorical forms of resilience drawn from the empirical science of resilience are positioned within NATO’s emerging frame for ‘layered’ resilience. This article reinforces the scientific debate that an optimal orientation to resilience leaves open the door for the transformative adaptation of function and identity when the single-equilibrium processes of resilience reach their limits. This article concludes with a normative perspective on how military and civilian resilience planning could support the development of NRPs that would amplify the Allies’ collective capacity to face shared security threats.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".