Multilingualism and Crossborder Mobility: A Critical Sociolinguistic Ethnography of Myanmar Migrants in Tengchong
Bibliographic record
Abstract
Drawing from the concept of linguistic entrepreneurship, this paper investigates how four Myanmar migrants in Tengchong mobilize various resources to invest in their multilingual repertoires for enhancing entrepreneurial opportunities, as well as how their language entrepreneurship experiences intersect with diverse social factors to influence the realization of their entrepreneurial aspirations. The findings based on semi-structured interviews indicate that Chinese-mediated multilingual competence serves as linguistic capital for Myanmar migrants in China to achieve their multilingual entrepreneurship, which can be converted into cultural, economic, and social benefits. On one hand, Chinese proficiency plays a significant role in the cross-border mobility and employment prospects of Myanmar migrants. It enhances the cultural identification of Myanmar migrants of Han Chinese to China, assists migrant workers in securing high-paying jobs, expands social networks, and facilitates career advancement and upward mobility. On the other hand, after migrating to China, Myanmar migrants continuously employ diverse strategies to improve multilingual skills and cultivate multilingual resource identities, thereby expanding their entrepreneurial networks. However, the successful transformation of their multilingual capital into entrepreneurial aspirations is not without problems. Despite displaying various entrepreneurial qualities in empowering their life trajectories in China, Myanmar migrants are confronted with different material constraints that may unintentionally hinder them from achieving their entrepreneurial dreams. This study can expand the scope of the existing scholarship of multilingualism and social mobility by examining the lived experiences of a cohort of Myanmar migrants in an under-explored but geopolitically important context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".