MétaCan
Menu
Back to cohort
Record W3110220580 · doi:10.1515/eujal-2019-0008

Ethnolinguistic Identity, Coping Strategies and Language Use among Young Hungarians in Slovakia

2021· article· en· W3110220580 on OpenAlexaff
László Vincze, Marko Dragojević, Jessica Gasiorek, Milica Miočević

Bibliographic record

VenueEuropean Journal of Applied Linguistics · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsMcGill University
Fundersnot available
KeywordsVitalityPsychologyCoping (psychology)PerceptionSocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.240
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Applied LinguisticsSame topicLinguistics, Language Diversity, and IdentityFrench-language works237,207