Translanguaging in higher education: experiences and recommendations of international graduate students from the Global South
Bibliographic record
Abstract
Canada is among the top three receiving countries for international students globally, and the leading sources of international students consist of countries in the Global South. Despite the multilingual reality of universities in Canada, most institutional language policies require only English or French to be used in instruction and assessment. The consequences of these policies include challenges in ensuring inclusive and equitable education. A translanguaging pedagogy has the potential to affirm and leverage the diverse language practices of international students, but it needs to be centered in the lived experiences, language practices, knowledge systems, and goals of a multilingual student body. This paper reports on the experiences and recommendations of international graduate students from the Global South related to pedagogical translanguaging in higher education. Data sources included interviews with 15 graduate students enrolled in the education faculty of a Canadian university. A thematic analysis of the data suggested that students’ translanguaging practices are restricted to informal spaces and ‘secret talk,’ and influenced by their instructors’ varied attitudes and language policies. Students’ recommendations include affirming translanguaging as a right and pedagogical resource for international students, incorporating translanguaging in academic writing, diversifying hiring practices, and providing training for instructors and prospective teachers.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".