Ethnolinguistic Identity, Coping Strategies and Language Use among Young Hungarians in Slovakia
Bibliographic record
Abstract
Abstract The purpose of the present paper was to investigate the propositions of ethnolinguistic identity theory among young Hungarian speakers in Slovakia. Specifically, we aimed to explore the role of ethnolinguistic identification, vitality and boundary permeability in coping with negative ethnolinguistic identities, and also how these effects impact language use. Self-report questionnaire data were collected among Hungarian-speaking secondary school students in (N = 311). The data were analyzed using a Bayesian moderated mediation analysis with informative priors for coefficients based on an earlier study. The results provided mixed support for the hypotheses. Ethnolinguistic mobility appeared to be an outcome of a complex process, where identification, vitality and permeability operate interactively; at the same time, ethnolinguistic competition was independent of the perception of vitality and permeability, but a sole consequence of strong Hungarian identification. In addition, the results indicated that identification, vitality and competency in the outgroup language were factors predicting language use, whereas there was no support for the anticipated mediating effect of coping strategies. Findings are discussed in relation to ethnolinguistic identity theory and to the particular qualities of the local context.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".