"Re-Imagining" the Homeland? Languages and National Belonging in Ukrainian Diasporas since the Euromaidan
Bibliographic record
Abstract
From the first days of the Euromaidan protests, Ukrainian diasporas around the globe took an active part in supporting democratic change in Ukraine. These diasporic communities actively used social media to “represent” their national identity, to promote their visions of Ukraine’s past and future, and to network and coordinate their actions. This paper argues that the events of the Euromaidan made Ukrainian diasporas in Western countries “re-invent” and “re-imagine” their national belonging. In these processes historical memory, language, and regional identifications play a crucial part within the continuum between conservative ethnonationalist identities and “civic” ones that try to accommodate the ethnic and linguistic diversity of Ukraine in the diasporic setting. This study reveals that “civic” identity elements became more visible across Ukrainian diasporas, but that Russian aggression somewhat haltered the acceptance of diversity and reinforced previously existing conservative sentiments.
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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 | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 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".