What Can Southern Multilingualisms Bring to the Question of How to Prepare Teachers for Linguistic Diversity in Canadian Schools?
Bibliographic record
Abstract
This chapter explores what the knowledge and expertise of southern and marginalized communities can bring to the question of contemporary linguistic diversity in Canadian schools. Global patterns of migration and mobility are increasingly altering language ecologies. Prior to European contact, Indigenous societies in the territory now known as Canada were characterized by a high degree of diversity, and interrelated economies often necessitated the learning of multiple languages. The Canadian settler population has also always been linguistically and culturally diverse. Despite government efforts to invent Canada as a society founded by two linguistic nations and efforts to suppress Indigenous and other minority languages, Canadian schools continue to be sites of linguistic diversity. While a multilingual Canadian population may not be new, the question of preparing teachers to support rather than suppress student multilingualism may be. This chapter is an exploration of what southern multilingualisms and diversities can bring to understanding questions of student diversity and to the development of teachers and curricula that offer equitable linguistic and epistemic access. Personal experiences and knowledge from southern and Indigenous communities also inform this exploration of linguistic diversity in education in the northern setting of the world where the researchers work together in teacher education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".