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Record W2745874810 · doi:10.1080/10357718.2017.1334190

The benefits of foreign policy bipartisanship revisited: lessons from two Canadian cases

2017· article· en· W2745874810 on OpenAlexaffabout
Kim Richard Nossal

Bibliographic record

VenueAustralian Journal Of International Affairs · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsQueen's University
Fundersnot available
KeywordsForeign policyOpposition (politics)NormativePoliticsPolitical scienceScholarshipArgument (complex analysis)CriticismPolitical economyPublic administrationLaw and economicsLawSociology

Abstract

fetched live from OpenAlex

Although foreign policy bipartisanship in Westminster systems is often heralded as a normative good, there is an emerging scholarship which suggests that a bipartisan approach to foreign and defence policy comes with considerable costs. This article seeks to join that debate. It does so by examining two contemporary foreign/defence policy issues in Canadian politics: the mission in Afghanistan from 2001 to 2014 and the efforts to replace the CF-18 Hornet flown by the Royal Canadian Air Force. These two cases do not offer clear conclusions about the normative argument about foreign policy bipartisanship. The embrace of a bipartisan approach to the Afghanistan mission confirms the criticism that bipartisanship can suppress public debate and did indeed distort a consideration of policy options. But the case of the CF-18 replacement suggests that there are significant costs if government and opposition replace a search for bipartisan consensus on key policy issues with an overt politicisation that seeks partisan advantage by ‘playing politics’ with foreign and defence policy issues, concluding that the quality of partisanship is a necessary condition to avoid the dysfunctions and costs of bipartisanship.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.076
GPT teacher head0.393
Teacher spread0.316 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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