Humanitarian, linguistic and narrative bordering in Georgia: migrations in the context of Russia’s war on Ukraine
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".