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Record W3035194416 · doi:10.1080/08865655.2020.1777887

Russian – Norwegian Borderlands: Three Facets of Geopolitics

2020· article· en· W3035194416 on OpenAlexvenueno aff
Andrey Makarychev, Anna Kuznetsova

Bibliographic record

VenueJournal of Borderlands Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsAnnexationMainstreamDiplomacyPolitical sciencePoliticsPolitical economySecuritizationNational securityNorwegianEconomySociologyLawEconomics

Abstract

fetched live from OpenAlex

This article aims to explore a paradoxical co-existence of various forms and models of trans-border interactions in areas of direct adjacency of Norway and Russia. Our main hypothesis is that the structural conditions of securitization that became dominant in NATO-Russia relations after the annexation of Crimea and the war in Donbas produce different effects all across the borderline, directly affecting borderland communities, including mobility, connectivity and public security. As our key point, we posit that the geopolitical conflictuality and the ensuing gaps and ruptures in military security are not automatically projected onto the level of “low” / grass-roots / local politics where there exists a public demand for expanding the existing spaces of interaction in such fields as cultural exchanges, environmental protection and people-to-people contacts. Apparently, the geopolitical divides are more visible and easily identifiable through the mainstream media, while other layers need a different optics allowing to spot various regimes of border functioning and peer into the complex construction of borders, where geopolitical divisions and partitions are counter-balanced by sub-national activities and initiatives discarding the logic of geopolitical conflict and alternating it with the grass-roots public / cultural diplomacy.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.057
GPT teacher head0.374
Teacher spread0.317 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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