Perceptions of New Speakers of Ukrainian Before and After the Full-Scale Invasion
Bibliographic record
Abstract
This study examines how the ideologies of the teaching of Ukrainian have shifted on the spectrum of purity/impurity since the beginning of the full-scale war. It analyzes Soviet teacher education materials and textbooks, Ukrainian textbooks, assessment materials, and online language courses before and after the 2022 full-scale invasion. It asks how state and non-state language education institutions approached learners and new speakers of Ukrainian before and after February 2022. The analysis is rooted in historical institutionalist scholarship that considers language ideologies contingent on institutional legacies. It shows that the purist language ideology in Ukraine can be traced to the Soviet Union and that formal language-teaching institutions have been slow to overcome it. It also shows that the full-scale war has weakened purism in non-state Ukrainian language teaching, where overcoming Soviet institutional legacies was easier. Increasing tolerance to nonstandard language signals increasing openness to the diverse ways Ukrainian can be spoken and Ukrainian identity performed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".