Relations of Language and Identity : Examining the eff ects of Mandarin education in the Xinjiang Uyghur Autonomous Region in China
Bibliographic record
Abstract
This paper discusses how, if at all, Mandarin education aff ects Uyghur students in terms of the potential correlation between language and identity in Xinjiang Uyghur Autonomous Region. Xinjiang is located in the north-west part of China and it has been experiencing violent incidents against government policies recently. The Communist Party of China (CPC) has applied various social policies with the aim of achieving a “harmonious society” through “ethnic unity” under the “Chinese nation” as a response to those incidents. Mandarin education is one of the ways CPC employs to achieve ethnic unity. In Xinjiang, the importance of acquiring Mandarin language is stressed to ethnic minorities, like Uyghur who are mostly Muslim. Through literature reviews, a site visit, and interviews with Uyghur, this paper reveals infl uence of Mandarin education on Uyghur-Han ethnic group relations and Uyghur identity. It also explores the validity of Mandarin education as means to realize ethnic unity and a so-called harmonious society. Language and identity are closely related to each other. For instance, language determines ethnic identity and identity encourages its holder to learn language. Mandarin education segregates Uyghur and Han and strengthens Uyghur identity because of this correlation. As this form of education differentiates Uyghur from Han and emphasizes the difference of Uyghur ethnic identity, current education overemphasizing Mandarin is not appropriate as means to achieve CPC’s goal of ethnic unity and a harmonious society. If the CPC wants to realize harmonious society as a multi-ethnic country, it should introduce education which esteems minority language and culture, and should promote mutual understanding from both the minority side and the majority Han side. In harmonious societies and multiethnic states, each ethnic group maintains its traditional language and culture. Minorities and Han should seek to understand one another through ongoing interactions and mutual acceptance of their cultural differences.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".