Multilingualism in the Canadian Educational Context: Identity, Belonging, and Translanguaging Pedagogies
Bibliographic record
Abstract
In this study, I examine the nature of multilingual education, which provides cultural, social, and economic affordances yet poses considerable emotional and academic challenges for learners. I explore how dominant languages impact multilingual learners’ identity and sense of belonging and identity negotiation, particularly within the Canadian educational context. Through a multiple case study involving four Iranian-Canadian minors and their parents, I use multimodal data collection methods, including semi-structured interviews with children and their parents, children’s writings, and multisemiotic representations, to capture the complexities of learners’ communicative repertoires. Participants’ narratives revealed tensions between their multilingual identities and the monolingual ideologies entrenched in educational systems. However, the study reveals that translanguaging pedagogy, which has emerged as a crucial pedagogical strategy, can enable learners to draw on their unitary communication competence without suppressing part of their linguistic repertoire to enhance understanding and reduce cognitive pressures. This research signals the imperative to operationalise translanguaging as a classroom practice. Dialogic tasks and teacher mediation that affirm multilingual expression can be embedded into everyday instructions. These findings illuminate the imperative for educational reform policies that go beyond classroom practices to address the broader monolingual and neoliberal ideologies, which prioritise the dominant state languages as pathways to prosperity. I argue that such reforms must not be tokenistic and must meaningfully engage with the sociocognitive and cultural challenges multilingual learners face to ensure that their diverse linguistic needs are fully supported.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
| opus | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".