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Implementation of Language Policy in School Education of the Chuvash Republic (late 1980s– 2019)

2019· article· en· W2995774561 on OpenAlexaboutno aff
Ivan I. Boyko, Dolgova Alevtina, Valentina G. Kharitonova

Bibliographic record

VenueВестник антропологии (Herald of Anthropology) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSociopolitical Dynamics in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLanguage policyPopulationPolitical scienceQuarter (Canadian coin)Public relationsSociologyEconomic growthPedagogyHistoryEconomicsLawDemography

Abstract

fetched live from OpenAlex

The article discusses some of the problems of language policy in the Chuvash Republic. Attention is paid to the Chuvash language teaching in educational institutions after the adoption of republican legislation on languages in 1990. This process was not simple, it was accompanied by a lack of understanding of the necessity of studying the Chuvash language in the regions of the republic where Russian population predomiates over the Chuvash one. For more than a quarter of a century, considerable experience has been gained in the organization and methods of teaching the Chuvash language, and despite the fact that it has barely become more widely spoken, it has become more familiar at the domestic and public levels. The transition to the voluntary learning of native languages began in the second half of 2017 and was accompanied by organizational difficulties. The article also gives opinions of the Chuvash language teachers on the problems of its teaching and usage in the family and social environment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.389
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2019
Admission routes1
Has abstractyes

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