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Record W4366760052 · doi:10.1093/isq/sqad024

Contested Strategic Cultures: Anglosphere Participation in the Coalition against ISIS

2023· article· en· W4366760052 on OpenAlexafffundabout
Justin Massie, Jonathan Paquin, Kamille Leclair

Bibliographic record

VenueInternational Studies Quarterly · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsUniversity of TorontoUniversité LavalUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMultinational corporationConceptualizationScholarshipLegislatureAlliancePoliticsForeign policyPolitical sciencePolitical economySociologyPublic administrationPublic relationsLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.452
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes3
Has abstractyes

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