Bibliographic record
Abstract
With the increasing global status of China’s economy, languages other than English education (LOTE) in China has experienced an unprecedented expansion size. The importance of offering language programs is particularly relevant and geographically significant in China’s border provinces. Yunnan, China’s Southwest border province, has been discursively positioned as an ideal space for cultivating South Asian and Southeast Asian language learners for China’s cross-cultural communication. However, while majoring in LOTE, Chinese students have to prove their proficiency in English both for academic attainment and employment prospect. This study examines how English is ideologically embedded in the learning process of LOTE learners in the context of China’s socioeconomic transformation and regional integration with its neighbouring countries. Adopting the concept of language, investment and ideology (Darvin & Norton, 2015), the study explores the English learning experiences of Chinese undergraduates majoring in LOTE in a Chinese border university in Yunnan. Findings reveal how LOTE learners attach great importance on English and see English as a symbolic capital which can be translated into academic credits and facilite their future educational and social mobility. Findings also demonstrate that LOTE learners take advantage of their learning strategies in English and transfer their learning strategies into their LOTE learning process. Based on the findings of the study, it is argued that LOTE students can be empowered to enhance their language proficiency by LOTE in the process of investment learning English and their investment can help them acquire additional multilingual ability and multiple identities. The study can prove pedagogical implications for curriculum of LOTE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".