Military Strategy of Middle Powers: Competing for Security, Influence, and Status in the 21st Century
Bibliographic record
Abstract
"Military Strategy of Middle Powers explores to what degree 21st-century middle powers adjust their military strategies due to changes in the international order, such as the decline in US power. The overarching objective of the book is to explain continuity and change in the strategies of a group of middle powers during the 21st century. These strategies are described, compared and explained through the lens of Realism. In order to find potential explanations for change or continuity, the strategies of eleven 'middle' powers are analysed (Canada, Germany, Italy, Spain, Australia, Brazil, Indonesia, South Africa, India, Japan, South Korea). This group of countries are considered similar in several important aspects, such as relative power capacity. When searching for potential explanations for different strategic behaviours among the middle powers, their unique regional characteristics are a key focus and, consequently, the impact of the structure and polarity, as well as the patterns of amity and enmity, of the regional context are analysed. The empirical investigation is focused on security strategies used since the terrorist attacks of 11 September 2001, which was one of the first major challenges to US hegemony. This book will be of much interest to students of military and strategic studies, foreign policy and International Relations, in general"--
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".