Bibliographic record
Abstract
Abstract The article examines ideologies behind linguistic conversion—a widespread transition to Ukrainian from Russian—which intensified in Ukraine after the onset of Russian aggression in 2014, and particularly after the 2022 full-scale invasion. Employing ‘new speakerness’ as a theoretical lens, the study draws on biographical interviews with twenty-one new full-time Ukrainian speakers recruited among participants in informal language-learning initiatives in Ukraine. The primary focus is on the ways in which the new speakers legitimise their ownership of the Ukrainian language: how they imagine their positions in the socially constructed traditional hierarchies of Ukrainian speakers, based on the mastery of the standard language, and what new ideologies arise out of their challenges. The findings reveal that, in most of the cases, traditional hierarchies are deconstructed as new ideologies prioritising fluency and elevating translingual practice emerge in the linguistic safe spaces of grassroots language courses and community clubs. (New speakers, language ideologies, linguistic conversion, suržyk, linguistic safe spaces, Russo-Ukrainian war)
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".