Contested Strategic Cultures: Anglosphere Participation in the Coalition against ISIS
Bibliographic record
Abstract
Abstract The study of multinational military interventions highlights the importance of four major factors to account for combat participation in US-led coalitions: threat perceptions, alliance considerations, domestic politics, and strategic culture. The latter, however, has been overlooked or uncorroborated by major cross-national accounts of coalition warfare. Building on the fourth generation of scholars working on strategic culture as well as legislative studies scholarship, we propose and put to empirical test a conceptualization of strategic culture that focuses on force conceptions, foreign policy roles, and domestic contestation of self-representations. Through a longitudinal and systematic qualitative content analysis of parliamentary debates that took place in the United Kingdom, Canada, and Australia from August 2014 to December 2017, the article finds that force conception and domestic contestation are best associated with variation in allied participation in US-led combat operations. In contrast, foreign policy roles are not found to shed light on allied military participation. We conclude that more cross-national and within-case analyses of strategic culture hold the potential to contribute to our understanding of the peculiarities of coalition operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".