National identity and beliefs about historical linguicide are associated with support for exclusive language policies among the Ukrainian linguistic majority
Bibliographic record
Abstract
We examined the idea that endorsement of state-level restrictive language policies can be understood as an ingroup-preserving behaviour driven by majority group members’ experiences of linguistic-based collective angst (i.e., concern about the future vitality of the ingroup’s language). We did so in the context of legislative reform aimed to enforce monolinguistic public education in Ukraine – a linguistically heterogeneous nation-state with a history of a foreign ethno-political domination. Specifically, we hypothesized that collective angst is most likely to be experienced when majority group members feel higher attachment to Ukraine (vs. glorification) and shared beliefs about historical linguicide of the Ukrainian language. Using data from a public opinion survey ( N = 774), we found support for the mediation model – higher attachment and beliefs about historical linguicide predicted increased support for restrictive policies directly and through collective angst, whereas glorification was found to be a non-significant predictor in this relation. Our results highlight the role of the specific content of protagonists’ social identities in predicting their support for cultural assimilation of ethnic minority groups within heterogeneous societies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".