LINCing language to critical multiculturalism : pursuing translingual pedagogies in English instruction for newcomers to Canada
Bibliographic record
Abstract
Many newcomers to Canada experience significant difficulties as they adjust to life in their new community, with few being more challenging than their journey to become English speakers. While Canada’s espousal of a welcoming, multicultural national identity, as well as its federally funded Language Instruction for Newcomers to Canada (LINC) program, suggest cohesive social integration, the historical consequences of the discourse of neoliberal multiculturalism and language ideologies of monolingualism and standardization that inform the LINC program have only served to assimilate and marginalize newcomers. The disruptions that COVID-19 brought to LINC classes only further complicated and exacerbated many of the issues that newcomers were already facing. However, despite these challenges, the incorporation of digital technologies into the LINC class as a way to adjust to the reality of the pandemic has created a new space outside of the class, yet still within the LINC community, through which a transformative, translingual pedagogy has the potential to take root. With appropriate guidance, the adoption of a translingual pedagogy has the potential to both work against the problematic discourses perpetuating within Canada, as well as improve the students’ English language learning outcomes by providing increased opportunities for digital literacy socialization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.042 | 0.013 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".