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Record W2981171739 · doi:10.1177/0020702019874798

Canada as a peninsula state: Conceptualizing the emerging geopolitical landscape in the 21st century

2019· article· en· W2981171739 on OpenAlexaffabout
Tsuyoshi Kawasaki

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGeopoliticsForeign policyChinaPoliticsAlliancePolitical sciencePeninsulaPolitical economyState (computer science)BureaucracyPublic administrationSociologyGeographyLaw

Abstract

fetched live from OpenAlex

An unprecedented geopolitical landscape, driven by the reduction of Arctic ice and the rise of China as “a Polar power,” is emerging. What does this mean for Canada, and how should Canada respond to it in a systematic and strategic manner? We need a coherent and holistic conceptual framework to answer these key policy questions. Yet, the current literatures do not offer us such a concept. In an attempt to fill the void, this article presents a vision that conceives of Canada as “a peninsula state” exposed to great power politics in its vicinity, involving China as a rising power as well as the United States and Russia as resident powers. Furthermore, it argues that Canada should be prepared for three kinds of strategic dynamics as it enters the game of great power politics: theatre-linkage tactics and wedge-driving tactics vis-à-vis China and Russia, as well as quasi-alliance dilemma with the United States. Moreover, in order for Canada to cope with this complex international environment effectively, this article calls for creating a cabinet-level unit to coordinate various federal bureaucracies’ foreign and security policies.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.077
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0110.025
Scholarly communication0.0110.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.318
Teacher spread0.309 · 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 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

Citations2
Published2019
Admission routes2
Has abstractyes

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