Implementation of Language Policy in School Education of the Chuvash Republic (late 1980s– 2019)
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".