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Record W4389160074 · doi:10.22215/cjers.v16i2.4148

No Great Russia without Greater Russia

2023· article· en· W4389160074 on OpenAlexvenueno aff
Tom Casier

Bibliographic record

VenueThe Canadian Journal of European and Russian Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsArgument (complex analysis)Great powerPower (physics)Political scienceIdentity (music)ExistentialismPolitical economyTerritorial integrityRussian federationNexus (standard)Foreign policyEconomic systemSociologyChinaLawPoliticsEconomicsSovereigntyAestheticsPhilosophy

Abstract

fetched live from OpenAlex

This paper argues that to understand the invasion of Ukraine, we need to have better insights into the Kremlin’s particular world view and Russia’s place within it. This view is based on a sense of entitlement to great power status, going hand in hand with an identity of itself as a country that extends beyond the actual borders of the Russian Federation. What makes the position unique is that the geopolitical and identity arguments are inseparable: in the Kremlin’s world view, Russia can only be a great power if it also exists as greater Russia. This structural factor is labelled the geopolitics-identity nexus. To explain why the invasion happened in 2022, three additional process factors are outlined: a radicalisation of the view of Ukraine as Russian lands, driven by the feeling of existential crisis when tensions over Ukraine escalated in 2014; an escalation of policy options resulting from consecutive failures in Russia’s Ukraine policy; and a reversal of the argument that Russia has to be a great power to exist within its 1991 borders into an argument that Russia has to expand its territory to be a great power.

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)0.002
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.010
Scholarly communication0.0050.004
Open science0.0000.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.060
GPT teacher head0.307
Teacher spread0.247 · 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
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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