Chinese whispers: international Chinese students’ language practices in an anglophone Higher Education context
Bibliographic record
Abstract
This study explored the language practices of a small group of international Chinese students in an anglophone Higher Education context where English was the medium of instruction. The context was the first year of an early childhood education course at an Australian university. Building on findings from research in conversation analysis on language alternation and medium of interaction, the analyses sought to unveil the students’ classroom verbal and nonverbal practices as they switched between Mandarin and English. Findings show that students’ preferred medium was monolingual: English for discussing taskwork and Mandarin for resolving disagreement or confusion, establishing understanding, and selecting a speaker. Alternation to Mandarin was accompanied by whispering and the embodied actions of ‘hiding’ behind the laptop while co-occurring laughter was used to signal a language switch or to index trouble or a delicate situation. These findings suggest that language choice was not simply a practice for restoring the preferred medium. Rather the students continued to speak Mandarin until the interactional motivation for its use was completed, which legitimized the use of their shared language. The paper ends with recommendations to inform pedagogy that is sensitive to the linguistic needs of international students in Higher Education in anglophone contexts.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| 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".