Education, language, and conflict in Myanmar's ethnic minority states
Bibliographic record
Abstract
Abstract To what extent did the democratic environment in Myanmar allow ethnic minority groups to promote their language and culture? After the opening of the political regime in 2011, Myanmar's ethnic minority groups placed high hopes in securing new rights and powers to manage their own states and preserve their culture. While the state implemented new opportunities for education of ethnic languages and culture, I argue that these small improvements are dwarfed by the continued Burmanization of ethnic minorities, and the strength of the Bamar‐dominated state. Despite the reform of the educational curriculum, the gains made are relatively modest. More effective decentralization and inclusion of these issues when discussing more genuine federalism would help to improve ethnic groups' ability to maintain their language and culture. This article draws on data from a survey conducted in 2019, as well as interviews in Chin, Kachin, and Karen states between 2015 and 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".