Myth and Reality in Learning Vietnamese at a China’s Border University
Bibliographic record
Abstract
With the implementation of Belt and Road Initiative and the cooperation between China and non-Anglophone countries, less commonly taught foreign languages (LCTFL) have been valorized at many Chinese universities particularly at China’s border provinces. Adopting Spolsky’s language policy as a theoretical framework, this study examines language learning experiences of Vietnamese majors at a China’s border province. Based on a longitudinal ethnography between October 2022 and March 2023, data were collected from semi-structured interviews with Chinese undergraduates and postgraduates majoring in Vietnamese, classroom observation, field notes and relevant written documents. Findings show that learning Vietnamese language has been discursively promoted as potential for educational upward mobility and employment project at institutional level. However, a close examination of Vietnamese majors’ experiences indicates that there are a series of inconsistencies between what is discursively promoted and what is actually practiced. The ideological interplay of learning Vietnamese between institutional promises and individual practices has been unpacked in relation to social, economic and cultural factors. This study can shed lights on language policy and planning for creating a better understanding of learning and teaching LCTFL in China’s border provinces.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".