Bibliographic record
Abstract
Abstract The complexity of the twenty-first century threat landscape contrasts significantly with the bilateral nuclear bargaining context envisioned by classical deterrence theory. Nuclear and conventional arsenals continue to develop alongside antisatellite programs, autonomous robotics or drones, cyber operations, biotechnology, and other innovations barely imagined in the early nuclear age. The concept of cross-domain deterrence emerged near the end of the George W. Bush administration as policymakers and commanders confronted emerging threats to vital American military systems in space and cyberspace. The Pentagon now recognizes five operational environments or so-called domains (land, sea, air, space, and cyberspace), and cross-domain deterrence poses serious problems in practice. This book steps back to assess the theoretical relevance of cross-domain deterrence for the field of international relations. As a general concept, cross-domain deterrence posits that the ways in which actors choose to deter affects the quality of the deterrence they achieve. Contributors to this book include senior and junior scholars and national security practitioners. Their chapters probe the analytical utility of cross-domain deterrence by examining how differences across, and combinations of, different military and nonmilitary instruments can affect choices and outcomes in coercive policy in historical and contemporary cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".