Multilingual Communication Experiences of Foreign Migrants in China During the Covid-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has generated a series of language-related challenges confronting linguistically diverse populations worldwide. Given that China has emerged as an ideal destination for international students and workers seeking upward mobility, it is essential to investigate how foreigners working in China get access to public health information. Adopting the concept of multilingual crisis communication, this study examines the multilingual communication experiences of a cohort of foreign workers working for one of the biggest nightclubs in the Southwestern region of China. Data were collected based on the semi-structured interview with five foreign dancers and their high-stake holders, including a Chinese boss and a Chinese dance director. It was found that foreign migrant dancers were confronted with various language barriers in understanding Putonghua and English-mediated communication resources. The finding also indicated that their access to public health information was facilitated by their use of translation applications, with the support of their Chinese friends and foreign colleagues whose multilingual repertoires constituted an essential medium for effective communication. This paper closes by providing practical suggestions, like offering other smaller languages and official language training services for foreign migrants of diverse linguistic backgrounds, mainly from peripheral 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".