The Commodification of Chinese in Thailand’s Linguistic Market: A Case Study of How Language Education Promotes Social Sustainability
Bibliographic record
Abstract
In recent decades, the commodification of the English language has aroused intensive research interest in the sociolinguistics on a global scale, but studies on the commodification of the Chinese language are relatively rare. Most studies take a critical approach in relation to its adverse impacts on minority rights and social justice. This study examined the language landscape in Chiangmai, Thailand, and the linguistic beliefs of local Thai Chinese language learners. Based on their feedback, this study investigated the commodification of Chinese language education in the community of Chinese language learners in Chiangmai. We found that from a less critical perspective, the commodification of a second language provides more accessible and affordable educational opportunities for learners, especially those from low-income families, and at the same time language proficiency can broaden learners’ career choices and provide employees with additional value in industries, such as tourism, commerce, and services. This finding implies that language commodification, rather than typically being associated with linguistic imperialism and unbalanced socio-economic status, can be a contributing factor in promoting higher-education availability and social sustainability in certain circumstances. There may be some mediating factors between the commodification of language and changes in the sustainable balance of language, opening up space for future research to explore.
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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 | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".