Shedding Russianness, recasting Ukrainianness: the post-Euromaidan dynamics of ethnonational identifications in Ukraine
Bibliographic record
Abstract
Euromaidan and the subsequent Russian military intervention brought about a perceptible change in ethnonational identifications of Ukrainian citizens. Based on three nationwide surveys from various years, the present article seeks to measure this shift and explore its underlying factors and mechanisms. My analysis reveals considerable changes in ethnolinguistic identifications, practices of language use, and preferences regarding language policies of the state, which can be seen as a kind of bottom-up de-Russification, a popular drift away from Russianness. At the same time, I demonstrate that changes in identifications by nationality and native language are related to changes in the perceptions of these categories; that is, that they should be conceptualized as measuring people’s perceived belonging to both ethnic groups and civic nations. In other words, as people are shedding their Russianness in favor of Ukrainianness, they are also changing the meaning of being Ukrainian.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".