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Record W3083606292 · doi:10.3390/su12187344

The Commodification of Chinese in Thailand’s Linguistic Market: A Case Study of How Language Education Promotes Social Sustainability

2020· article· en· W3083606292 on OpenAlexaff
Shujian Guo, Hyunjung Shin, Qi Shen

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCommodificationSociologySociology of languageSociolinguisticsLocal languageSocial scienceLanguage educationEconomic growthLinguisticsEconomicsPedagogyEconomyComprehension approach

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.441
Teacher spread0.409 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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