An Ethnographic Study of Linguistic Landscapes at China-and-Vietnam Border
Bibliographic record
Abstract
Along with China’s Belt and Road Initiative, China is engaging itself with its neighboring countries and many border cities have been strategically positioned as trading nodes linking China to the outside world. This is particularly true with Hekou, a minority-centered border town between China and Vietnam where local minority languages, Chinese, Vietnamese and English are displayed at various spaces. Adopting a critical sociolinguistic ethnography (Heller, 2006; Li, 2017), this study focuses on the intersection of language practices and ideologies by examining the language use and language choices displayed both in public and private signs. Data were collected through linguistic signs displayed at Hekou and individual interviews with local people. Findings indicate that Chinese as China’s official language enjoys the most visibility, and English, though considered as a lingua franca, only acquires symbolic value rather than being used for daily communication at the border town. In contrast, Vietnamese, as a newly emerged foreign language, is acquiring cultural and economic capitals for the local people’ educational and employment opportunities. As a minority-centered border town, the visibility of minority languages on cultural events stands both for tourism boom and for border integrity. The study provides a new context for understanding multilingual practices and China’s border language planning and management in the context of China’s cooperation with Southeast Asian countries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".