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Record W4411325444 · doi:10.1080/15387216.2025.2516839

Humanitarian, linguistic and narrative bordering in Georgia: migrations in the context of Russia’s war on Ukraine

2025· article· en· W4411325444 on OpenAlexaff
Gaëlle Le Pavic, Ekaterina Korableva

Bibliographic record

VenueEurasian Geography and Economics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPost-Soviet Geopolitical Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsNarrativeContext (archaeology)HistoryPolitical scienceLinguisticsGeographyEconomyEthnologyAncient historyArchaeologyEconomics

Abstract

fetched live from OpenAlex

This article examines migration to Georgia triggered by Russia’s war against Ukraine, focusing on the differing experiences of Ukrainian and Russian migrants, as well as Belarusian migrants, including those who left their country earlier due to the political crisis of 2020. We explore how humanitarian, linguistic, and narrative bordering shape the lives of these migrants, with Georgia emerging as a significant site of encounter. Using qualitative data collected in 2022 and 2023 – including interviews, focus groups, observations, and visual ethnography – we analyze the influence of historical, cultural, and social factors on these bordering practices. Firstly, we show how humanitarian bordering is shifting based on the perceived innocence of the beneficiaries. Secondly, we examine how linguistic bordering operates in the context of Russia’s war-induced migration to Georgia, where the Russian language serves both as a connector among migrants and with the host society, while simultaneously being associated with the aggressor country. Thirdly, we explore how narrative bordering operates through the growing prominence of the “occupation narrative” in Georgian society – the narrative adopted by some migrants while rejected by many of those deemed “occupied” in Abkhazia.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.268
Teacher spread0.258 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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