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Record W4396674574 · doi:10.1080/11926422.2024.2346902

Nuanced futures: Canadian and US defence in the North American Arctic

2024· article· en· W4396674574 on OpenAlexaffabout
Thomas Hughes, James Fergusson, Andrea Charron

Bibliographic record

VenueCanadian Foreign Policy Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of ManitobaMount Allison University
Fundersnot available
KeywordsArcticFutures contractPolitical scienceThe arcticChinaCompetition (biology)Political economyLawBusinessSociologyOceanographyEcology

Abstract

fetched live from OpenAlex

Canada and the United States have a shared interest in North American defence, and both have committed to spending on Arctic defence capabilities. Defence interactions with China and Russia, and the development of new weapons systems, have put a spotlight on Arctic defence. Much of the resulting activity is preparation for potential, future competition rather than concerns about confrontation in the short term. Nevertheless, preparation discrepancies between Canadian and US approaches to Arctic defence become apparent. In particular, the United States takes a more forward-leaning military approach to Arctic security than does Canada, with a defence-centric military posture. These differences are not insurmountable, and shared understanding of the most significant defence threats related to the North American Arctic, in addition to the long-standing NORAD connection, represent solid foundations. Nevertheless, differences represent areas of friction that could be exploited by adversaries and Canada–US dialogue to minimize the effect of this non-alignment in approaches will be critical.

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)0.004
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: none
Teacher disagreement score0.134
Threshold uncertainty score0.974

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0400.011
Scholarly communication0.0120.004
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.000

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 head0.017
GPT teacher head0.296
Teacher spread0.279 · 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
Published2024
Admission routes2
Has abstractyes

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