The Multiple Ideologies of Shaping Cambodian Students’ Chinese Language Learning Experiences
Bibliographic record
Abstract
In light of the increasing prominence of China’s Belt and Road (B&R) Initiatives and China’s soft power projection to its neighboring countries, China's relations with Southeast Asian countries are getting closer. In recent years, a large number of Cambodian students have come to China for higher education. Informed by the theories of linguistic capital and language ideology, the present study aims to study the the macro-social factors mediated in the Cambodian students’ Chinese language learning experiences. For current study, the data was collected from Guizhou Minzu University and Qiannan Normal University for Nationalities through semi-structured interview, questionnaire, online interactions, and the collection of linguistic autobiographies as well as other relevant documents and materials. The findings of the study show that there are four main factors influencing Cambodian students’ higher education in China: (i) political factors: national and governmental policies; (ii) cultural factors: historical influences; (iii) educational factors: influences of schools and communities, parental strategies; (iv) economic factors: employment prospects and tourism. Based on the findings mentioned above, the study suggests that given the rapid increasing number of Cambodian international students in China, it is imperative for Chinese government and universities to consider how to better meet Cambodian international students’ study needs and employment prospects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".