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Record W2884361826 · doi:10.1177/0020702018786080

Canada, NATO, and Global Russia

2018· article· en· W2884361826 on OpenAlexaffabout
Nicole Jackson

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsNorth Atlantic TreatySoft powerPolitical scienceRhetoricTreatyRoot (linguistics)Power (physics)Process (computing)Quality (philosophy)Test (biology)International relationsPublic relationsSociologyPolitical economyLawAllianceEpistemologyPoliticsComputer science

Abstract

fetched live from OpenAlex

Today Russia poses significant challenges that require sophisticated responses from both Canada and the North Atlantic Treaty Organization (NATO), yet more research is needed on almost all aspects of policy development. Academic experts on NATO and Russia could contribute significantly to this process. To this end, collaboration and engagement among those experts with each other’s literature would be highly beneficial. Appropriate methodologies must be developed to answer questions about Russia’s specific intentions, test the assumptions upon which NATO and Canada’s policies are founded, and discover and respond to the root causes of Russia’s discontent. Policy options should be based on detailed knowledge of global security dynamics, as well as high-quality analysis about Russia’s rhetoric and its varied use of hard, soft, and sharp soft power in regional and global cases. A research network on these topics could help decision-makers respond to these complex developments by approaching them through “the eyes of our adversaries,” clarifying the big picture of hybrid warfare and also the micro-level details.

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)0.003
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 score0.064
Threshold uncertainty score0.464

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.005
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.008
GPT teacher head0.309
Teacher spread0.301 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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Same venueInternational Journal Canada s Journal of Global Policy AnalysisSame topicEuropean and Russian Geopolitical Military StrategiesFrench-language works237,207