Multilingualism in Canada: Policy and Education in Applied Linguistics Research
Bibliographic record
Abstract
Increasing multilingualism in Canada has captured the interest of applied linguists who investigate what it implies for policy and educational practice. This article provides a review of recent discussions of Canadian policy in the literature, current research on multilingual learners, and emerging innovations in multilingual pedagogies. The literature on policy indicates that some researchers treat policy as text and identify disjunctions between policy documents and the reality of a linguistically and culturally diverse population, while others view it as discursive practice and document how policy is constructed locally through language in response to a changing environment. The research on multilingual learners is based primarily on field-based reports that reveal how multilingual language practices are complex, dynamic, and ideological, and are tied to identity construction. The growing number of innovations in multilingual pedagogies suggests that more educators are beginning to see identity work and multimodal literacies as central to teaching students of diverse origins. This article concludes that there is a gap between official language policy and research on multilingualism in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".