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Record W2588529164 · doi:10.1177/0020702017692609

Understanding Putin: The politics of identity and geopolitics in Russian foreign policy discourse

2017· article· en· W2588529164 on OpenAlexaff
Kari Roberts

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsMount Royal University
Fundersnot available
KeywordsForeign policyAnnexationGeopoliticsNorth Atlantic TreatyMilitantNarrativePoliticsIdentity (music)Political sciencePolitical economyForeign relationsInternational relationsForeign policy analysisTreatySociologyLaw

Abstract

fetched live from OpenAlex

Russia’s 2014 annexation of Crimea and the subsequent deterioration in its relations with the West have led many analysts to adopt a narrow view of Vladimir Putin’s foreign policy motivations, chalking them up to old-school geopolitics. This paper makes the case that the traditional structural explanations for Russian foreign policy that are dominant within the discipline of international relations do not adequately consider the influence of identity in Putin’s emerging foreign policy narrative. Putin’s narrative is shaped by, and shapes, a discourse about cultural and historical ties with Russian borderlands, as well as by the cultural and security vulnerabilities generated by the West’s treatment of Russia, evidenced by the expansion of the North Atlantic Treaty Organization (NATO). This discourse has underscored a more militant foreign policy turn under Putin in which he is prepared to protect and defend Russia’s interests at high cost; Russia’s actions in Crimea exemplify this. This connection between identity and foreign policy in Putin’s Russia demands attention if we hope to gain a better grasp of Russian foreign policy under his leadership.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.059
GPT teacher head0.387
Teacher spread0.328 · 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 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

Citations58
Published2017
Admission routes1
Has abstractyes

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