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The problem of multilingualism and linguistic diversity in the European society

2019· article· en· W2968137007 on OpenAlexaboutno aff
Olena Byndas

Bibliographic record

VenueBulletin of Luhansk Taras Shevchenko National University · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsMultilingualismLanguage policyDiversity (politics)Linguistic diversityPolitical scienceOpenness to experienceLinguisticsState (computer science)SociologyLawPedagogyPsychologyComputer science

Abstract

fetched live from OpenAlex

The article investigates the phenomenon of multilingualism and linguistic diversity in Europe. Despite the challenges posed by differences in the implementation of policies and practices of multilingualism in different states, the comparative data, presented in this study, are a rich source for cross-national review. The author notes that together with such concepts as respect for the individual, tolerance, openness to other cultures, the linguistic diversity of folks in the European society is one of its key values. Therefore, the activities of the European Community are aimed at encouraging and supporting many languages. Obviously, the language policy in Ukraine should be conducted precisely in the light of the prospect of the European integration. Indeed, becoming a full member of the united Europe our state will be able to exist under the appropriate conditions. Together with the experts, we came to the conclusion that our country should make a decision that will correspond to its historical tradition, heritage, requirements of modernity and the will of the Ukrainian people. Either we will follow the example of countries that are officially monolingual, like France or the United Kingdom, or we will use the experience of officially multilingual states like Switzerland, Belgium, Canada, etc., or our people will choose their own way in the language policy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.202
Teacher spread0.182 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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