A Critical Ethnography of Myanmar Migrants’ Grassroots Multilingualism at a Chinese Massage Parlour
Bibliographic record
Abstract
While China is broadening its gateway into South Asia and Southeast Asia, millions of foreign migrant workers cross the border and seek their transnational fortune in China’s border provinces. However, within the existing literature in migrant workers in China, language is rarely a research target in itself. As one of the important social actors language plays a key role shaping migrant workers’ life trajectories. Adopting Spolsky’s language policy theory and following the critical ethnography with migrant workers (Han, 2013; Mathews, 2011), this study explores the interplay of national polices of massage parlour management at a macro level, employers’ stipulations of managing Myanmar migrants at a meso level and Myanmar migrants’ language practices at micro level. Grounded upon critical sociolinguistic ethnography, data is collected from a China’s massage parlour at border town through the participant observation in and out of massage parlour, field notes, semi-structured interviews and documents. The study probes into how Chinese geopolitics of the wider process of regional development facilitates or constrains Myanmar migrants, how they mobilize social resources to expand their multilingual repertoires and how Chinese employer manages Myanmar migrants in language and life aspects. Findings reveal that there is no specific language policy at the recruitment stage. However, when Myanmar migrant workers start to work, language emerges as implicit but powerful medium streaming the likelihood of upward mobility. Other social factors, such as gender, nationality, religion and class also influence their mobility and integration into China’s local society. The study expands the understanding of language management and grassroots multilingualism in the context of globalization from below. Also the study provides implications on language policy making, migrants integration and education for migrants of multilingual backgrounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".