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INTERCULTURAL COMMUNICATION AND LANGUAGE POLICY IN MULTICULTURAL COUNTRIES

2025· article· W4415936144 on OpenAlexaboutno aff
N. Hasiuk, I. A. Sakhnevych

Bibliographic record

VenueInternational Humanitarian University Herald Philology · 2025
Typearticle
Language
FieldSocial Sciences
TopicGlobalization and Cultural Identity
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismLanguage policyIntercultural communicationMultilingualismPublic policyWork (physics)First language

Abstract

fetched live from OpenAlex

The article examines the issues of language policy and their impact on shaping intercultural communication within multicultural societies.In today's globalized world, increasing migration flows, expanding international contacts, and intensified cultural interaction highlight the need for balanced language policies that can ensure the harmonious coexistence of diverse ethnic and linguistic groups.The paper examines both successful examples of language policy implementation (Canada, Finland, Switzerland) and problematic cases (France, Belgium, Ukraine), thus revealing the impact of languagerelated decisions on social integration, the preservation of cultural identity, and the democratic development of states.Particular attention is given to the Ukrainian context, where language policy has long been at the center of social and political debates, reflecting complex processes of nationbuilding and the search for equilibrium between promoting the state language and protecting minority language rights.The methodological framework combines analytical, comparative, and interdisciplinary approaches.The empirical basis includes international documents (the European Charter for Regional or Minority Languages, UNESCO and Council of Europe reports), national legislative acts (in particular, the Law of Ukraine "On Ensuring the Functioning of the Ukrainian Language as the State Language"), as well as academic works by leading scholars in the field.The analysis of practical case studies made it possible to identify key challenges, such as the risk of linguistic space fragmentation, the marginalization of minority languages, and the potential for social tension caused by excessive unification of linguistic practices.The findings emphasize that effective language policy should take into account both state interests and minority rights, ensuring equal access to education, information, and cultural life.Balanced strategies contribute to the preservation of cultural heritage, the establishment of open intercultural dialogue, and the strengthening of democratic foundations of civic coexistence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.296
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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