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Record W2943712860 · doi:10.1017/s0008423919000040

Why Canada Goes to War: Explaining Combat Participation in US-led Coalitions

2019· article· en· W2943712860 on OpenAlexaffabout
Justin Massie

Bibliographic record

VenueCanadian Journal of Political Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAllianceInterventionism (politics)Political scienceLegitimacyCredibilityPolitical economyMultinational corporationNational securityPublic opinionGovernment (linguistics)IdeologyPublic administrationLawInternational relationsSociologyPolitics

Abstract

fetched live from OpenAlex

Abstract Canada has taken part in six wars since 1945, all of which have been conducted under US leadership. Despite such military interventionism, there have been no systematic comparative analyses of Canada's decisions to take part in US-led wars. The objective of this article is to develop and test a theoretical framework about why Canada goes to war. More specifically, it seeks to account for variations in Canada's provision of combat forces to multinational interventions led by the United States. It assesses leading theoretical explanations by examining five post–Cold War cases: the wars in Kosovo, Afghanistan and Libya; the war against ISIS; and the refusal to take part in the invasion of Iraq. The article concludes that Canada's willingness to go to war is shaped primarily by a desire to maintain transatlantic alliance unity and enhance Canada's alliance credibility. Threats to national security, the legitimacy of the intervention, government ideology and public opinion are not found to consistently or meaningfully shape Canadian decisions to take part in US-led wars.

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)0.003
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 score0.900
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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