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Record W2799601747 · doi:10.3138/ijcs.55.09

Canada's World: The Long Shadow of Geopolitics

2017· article· en· W2799601747 on OpenAlexaffvenueabout
Randall Germain

Bibliographic record

VenueInternational Journal of Canadian Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeopoliticsPoliticsChinaPolitical scienceShadow (psychology)EconomyDiversification (marketing strategy)Political economyDevelopment economicsInternational tradeEconomicsBusinessLaw

Abstract

fetched live from OpenAlex

Canada has long been subject to the powerful cross-pressures of geopolitics and global demand for its bountiful natural resources. Looking ahead over the next quarter century, I expect that the most important impact on Canada's relations with the world will stem from geopolitical developments in the United States and Asia. Not only will demand for natural resources and commercial products from these two parts of the world exert an ineluctable influence over Canada's economic development, but so too will the fallout generated by the politics of China's rise and America's response to this new force. We might call this the shadow of geopolitics. As Canada attempts to diversify its economy away from an overreliance on the American market, rising Sino-American tensions will effectively shrink geopolitical space in the most important new market for Canadian trade and investment. The paradox is that as Canada is drawn into economic flows connected to Asia's political economy, it will also slide into the crosshairs of rising Sino-American geopolitical tensions. In other words, to the extent that Canada reduces its economic vulnerability to the United States, it will become more susceptible to geopolitical vulnerabilities in Asia. Ironically, over the next 25 years or so, economic diversification may well generate geopolitical vulnerability for Canada in its global relations.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.050
GPT teacher head0.362
Teacher spread0.312 · 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 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

Citations2
Published2017
Admission routes3
Has abstractyes

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