The Impact of Language Policy on the Development of Bilingual Education for Minorities in China
Bibliographic record
Abstract
This article talks about the development of language policies that were established since the foundation of People’s Republic of China in 1949. We consider that the language policy as the implementation of governing party’s linguistic sovereignty, which is related to political, educational, economic and social construction. As one of a number of multi-ethnic countries in the world, China has to face the problems of improving the quality and effectiveness of bilingual education for minorities. Compared with mass education, the bilingual education has specific purpose not only basically to transmit universal knowledge and educate its youth, but also to make a contribution for a harmoniously multicultural society via the benefit of maintenance of language diversity, language practice and language planning. According to the different languages used in bilingual education, normally bilingual education is distinguished into two categories in China, bilingual education for minorities and bilingual education for foreign languages. Hereby we are going to discuss the bilingual of minority language and Mandarin. Specifically, we take the development of bilingual education of Tibetan areas in west of Sichuan Province as a case study, analysing the connection between the main four phases of bilingual education for Tibetan and the implementation of language policy in past sixty years. Obviously, the national policy has an important effect on the use of minority languages for their education and family life. Although the bilingual education cannot be improved only by legislation, still the complete laws allow the educational activities for minorities’ languages and culture to obtain more public recognition to retain it. The implementation of national bilingual education should have specific national goals, educational purpose and language achievement, therefore giving it clear direction for a successful outcome.
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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 | 0.003 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".