Linguistic and Social Equity for Yughur and Kyrgyz National Minorities in Northwest China
Bibliographic record
Abstract
For linguistic minorities, language-in-education policy strongly influences perceptions of educational quality. Ranging from minority-language instruction to dominant-language instruction, the latter is often justified by officials by parental preference and its supposed educational and employment benefits. This chapter investigates these claims via two extreme cases, the Kyrgyz and Yughur minority nationalities of west China, who have experienced minority-language and dominant-language education, respectively. Using educational and occupational statistics and qualitative data on community perspectives, we present evidence that both communities desire first language maintenance and Chinese proficiency, finding little difference between models in educational attainment and occupational outcomes. However, the minority-language model is associated with low Chinese proficiency, and the dominant-language model with Yughur language loss, with clear implications for the recent introduction of experimental “bilingual education,” a dominant-language model taught by bilingual teachers, among Kyrgyz. The chapter concludes by cautioning against both monolingual approaches, calling for the investigation of alternative language-in-education models, such as dual language maintenance bilingual education, as a means to promote quality minority education through balanced learning of both minority languages and the national language. The chapter ends with an argument for increased minority community involvement in decisions affecting their children’s schooling in dialogue with researchers and policymakers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".