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Record W2774404023 · doi:10.1177/0020702017740157

From Ottawa to Riga: Three tensions in Canadian defence policy

2017· article· en· W2774404023 on OpenAlexaboutno aff
Alexander Lanoszka

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsNorth Atlantic TreatyAllianceMultinational corporationCold warPolitical scienceDeterrence theoryTreatyPolitical economySoftware deploymentInternational tradeLawSociologyPoliticsEconomics

Abstract

fetched live from OpenAlex

In June 2016, Canada joined the United States, Great Britain, and Germany in becoming a Framework Nation that leads a multinational battalion-sized battlegroup in Latvia. Canada thus appears to be reprising the role it played during the Cold War as a leading participant in North Atlantic Treaty Organization deterrence and reassurance initiatives in Europe. Yet the three tensions that made Canada reduce its military commitments to allies over the course of the Cold War might resurface in the Baltic region. These three tensions relate to conventional specialization amid alliance nuclearization, low defence spending despite that specialization, and the potential decoupling of Canadian security interests from those of its European partners. Canada might find itself lacking the willingness and ability to sustain the tasks attending the Latvia deployment if the threat environment intensifies.

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)0.005
metaresearch head score (Gemma)0.013
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: none
Teacher disagreement score0.565
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.010
Science and technology studies0.0500.012
Scholarly communication0.0210.004
Open science0.0040.006
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0130.001

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 head0.016
GPT teacher head0.351
Teacher spread0.335 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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