The Acquisition of English Speaking Skills of Small Traders in Hanoi’s Old Quarter
Bibliographic record
Abstract
Abstract: The recent increase in the number of foreign visitors to Vietnam highlights the necessity for the improvement of English speaking skills of small traders in Hanoi’s Old Quarter - a popular tourist destination in Vietnam, where English is pivotal in both trading and promoting Vietnamese culture. In that context, this research explores how these traders could acquire their English speaking skills in their own living contexts. Adopting both qualitative and quantitative methods, particularly observation, interviews with small traders (n=23) and survey questionnaires combined with interviews with foreigners (n=100), the research has reached two major conclusions. First, unlike popular assumptions that small traders learn English through contact with foreigners, the sources of their English acquisition were much more diverse. Secondly, small traders were expected to speak English well not only to carry out transactions but also to aid foreigners in a wide range of functions, ranging from navigating through the streets to better understanding Vietnamese culture. However, the English speaking skills of these traders were often found insufficient in terms of grammatical, discourse, and sociolinguistic competences. From the collected data, the article suggests a number of different ways to enhance the small trader’s acquisition of English speaking skills.Keywords: Small traders, Hanoi’s Old Quarter, English language acquisition, international tourism.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".