Multilingualism in Canadian schools: Myths, realities and possibilities
Bibliographic record
Abstract
Bilingualism and multiculturalism have for four decades been official ideologies and policies in Canada but, as is often the case, the implementation and outcomes of such government policies nationally are less impressive than the rhetoric would suggest. This article reviews the political, theoretical and demographic contexts justifying support for the learning and use of additional languages in contemporary Canadian society and schools, and summarizes research demonstrating that bilingualism and multilingualism are indeed cognitively, socially, and linguistically advantageous for children (and adults), as well as for society. The five studies in this special issue are then previewed with respect to the following themes that run across them: (1) the potential for bilingual synergies and transformations in language awareness activities and crosslinguistic knowledge construction; (2) the role of multiliteracies and multimodality in mediated learning; and (3) the interplay of positioning, identity, and agency in language learning by immigrant youth. The article concludes that more Canadian schools and educators must, like the researchers in this volume, find ways to embrace and build upon students’ prior knowledge, their creativity, their collaborative problem-solving skills, their potential for mastering and manipulating multiple, multilingual semiotic tools, and their desire for inclusion and integration in productive, engaging learning communities.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.039 | 0.046 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".