Plurilingual pedagogies at the post-secondary level: possibilities for intentional engagement with students’ diverse linguistic repertoires
Bibliographic record
Abstract
This paper draws draw on conceptualisations of language as heteroglossic to examine whether and how multilingual practices and plurilingual pedagogies are enacted as instructional strategies in two multilingual English-medium universities in western Canada. Multilingual educational contexts have the potential to comprise ‘translanguaging spaces’ [Li, W. (2018). Translanguaging as a practical theory of language. Applied Linguistics, 39(1), 9–30. doi:10.1093/applin/amx039], wherein educators and students mobilise a range of semiotic resources for teaching and learning purposes. From a monolingual paradigm, such practice is often seen as interference or deficit; however, from a multilingual paradigm, this practice is seen as legitimate and unrestricted, with students free to use their linguistic resources as they wish to their own benefit. To conceptualise and analyse engagement with multilingual practice, we draw on Cenoz and Gorter’s [(2017). Minority languages and sustainable translanguaging: Threat or opportunity? Journal of Multilingual and Multicultural Development, 38(10), 901–912. doi:10.1080/01434632.2017.1284855] distinction between ‘spontaneous’ and ‘intentional’ translanguaging. To assist faculty to observe, act and reflect on implementation of plurilingual pedagogies, we propose a three-dimensional matrix comprising axes of (1) faculty- and student-initiated; (2) planned and spontaneous engagements with plurilingualism; and (3) plurilingualism as either a scaffold or a resource for curriculum, pedagogy and assessment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".