Myanmar Language Learning Experiences at China’s Frontier
Bibliographic record
Abstract
In the context of China’s implementation of Belt and Road Initiative, the development of LOTE (languages other than English) in China comes into a revitalization era. LOTE play important role acting as a bridge linking China to the other countries. Since 2009, Yunnan has been discursively constructed as a bridgehead for China to cooperate with Southeast Asian country and the education of LOTE with a particular focus on the Southeast Asian languages has experienced an unprecedented expansion size. This study explores the learning experiences of Chinese postgraduates majoring in Myanmar language in a Chinese border university in Yunnan. Findings reveal how Chinese students perceive the values of learning Myanmar language, what learning challenges they encounter and how they exert their agency to overcome their learning difficulties. The study reveals that Chinese students’ investment in learning Myanmar has been shaped by their access to various resources mediated in multiple social forces across time and space. The study can shed some lights on providing pedagogical implications for enhancing the learning outcomes of LOTE in China.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".