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Record W2216361622 · doi:10.1080/11926422.2015.1070275

A fearful asymmetry: Diefenbaker, the Canadian military and trust during the Cuban missile crisis

2015· article· en· W2216361622 on OpenAlexafffundabout
Michael C. Urban

Bibliographic record

VenueCanadian Foreign Policy Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrime ministerConceptualizationPolitical sciencePoliticsMissileState (computer science)Political economyDivergence (linguistics)LawSociologyEngineering

Abstract

fetched live from OpenAlex

/RésumésCanada's actions during the Cuban missile crisis open a revealing window onto the Canada—USA relationship and the sub-state influences therein. By refusing a US request to raise the alert status of Canadian forces, Prime Minister John Diefenbaker precipitated one of the worst crises in the modern Canada-USA relationship; by secretly defying this refusal, the Canadian military leadership ensured that Canadian forces provided greater military support to the US than any other ally did. In this article, I argue that a significant divergence in the extent to which the Canadian Prime Minister and military leadership trusted their US counterparts represents a critical part of any explanation of this bifurcated response. In so doing, I rely on a new conceptualization of trust and how it influences decision-making in international politics. By applying this model, I develop an explanation for Canada's contradictory behavior that is superior to existing explanations of Canadians’ decision-making during this crisis.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0240.011
Scholarly communication0.0090.003
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.296
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2015
Admission routes3
Has abstractyes

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