Plurilingual Students' Practices in a Canadian University: Chinese Language, Academic English, and Discursive Ambivalence
Bibliographic record
Abstract
We present findings from a 1-year study of students’ plurilingualism across the disciplines in Canadian higher education. We analyze how students exercise their plurilingual competence, focusing on the use of Chinese languages as tools for learning at a university in Metro Vancouver, Canada. The following data are presented: field notes taken during classroom observations, transcripts of recordings of students using Chinese languages while working collaboratively, and semistructured interviews with students in which they discuss how they use languages for learning. We weigh the creative and instrumental use of Chinese languages as tools for learning against the dominance of academic English. We suggest that the tension between the use of Chinese languages during the process of learning and academic English for assessment underlies the ambivalence around which student participants perceive and practice plurilingualism in higher education. Nous présentons les conclusions d’une étude d’une durée d’un an sur le plurilinguisme estudiantin à travers les disciplines de l’enseignement supérieur canadien. Nous analysons la façon dont les étudiantes et étudiants exercent leur compétence plurilingue en nous concentrant sur l’utilisation de langues chinoises comme instruments d’apprentissage dans une université du district régional du Grand Vancouver au Canada. Les données suivantes sont présentées : notes prises lors d’observations en classe, transcriptions d’enregistrements d’étudiantes et étudiants communiquant en langues chinoises dans le cadre de travaux en collaboration, et entrevues semi-structurées au cours desquelles ils discutent de la façon dont ils utilisent les langues pour apprendre. Nous établissons une pondération entre l’utilisation créative et instrumentale des langues chinoises comme outils d’apprentissage et la domination de l’anglais académique. Nous suggérons que la tension qui existe entre l’utilisation des langues chinoises en cours d’apprentissage et l’utilisation de l’anglais académique pour l’évaluation souligne l’ambivalence dans laquelle baignent les perceptions et les pratiques plurilingues des étudiantes et étudiants de langues chinoises au niveau de l’enseignement supérieur.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.037 | 0.021 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".