Chapter What Can Southern Multilingualisms Bring to the Question of How to Prepare Teachers for Linguistic Diversity in Canadian Schools?
Bibliographic record
Abstract
This book brings to life initiatives among scholars of the south and north to understand better the intelligences and pluralities of multilingualisms in southern communities and spaces of decoloniality. Chapters follow a longue durée perspective of human co-existence with communal presents, pasts, and futures; attachments to place; and insights into how multilingualisms emerge, circulate, and alter over time. Each chapter, informed by the authors’ experiences living and working among southern communities, illustrates nuances in ideas of south and southern, tracing (dis-/inter-) connected discourses in vastly different geopolitical contexts. Authors reflect on the roots, routes and ecologies of linguistic and epistemic heterogeneity while remembering the sociolinguistic knowledge and practices of those who have gone before. The book re-examines the appropriacy of how theories, policies, and methodologies ‘for multilingual contexts’ are transported across different settings and underscores the ethics of research practice and reversal of centre and periphery perspectives through careful listening and conversation. Highlighting the potential of a southern sociolinguistics to articulate a new humanity and more ethical world in registers of care, hope, and love, this volume contributes to new directions in critical and decolonial studies of multilingualism, and to re-imagining sociolinguistics, cultural studies, and applied linguistics more broadly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".