Heterogeneity and a <i>Sociolinguistics of Multilingualism</i>: Reconfiguring French Language Pedagogy
Bibliographic record
Abstract
Abstract This article focuses on the current state of the field of French language pedagogy (FLP) and education in Canada. The author provides a brief history of French as a Second Language (FSL) education and Official bilingualism in Canada to demonstrate the social, historical, political and ideological dimensions that have shaped (and continue to shape) ways of thinking about FLP. In relation to the state of current research, this article highlights some of the contributions sociolinguistic research brings to contemporary thinking about multilingualism as it relates to FLP, by examining what the author refers to as the sociolinguistics of multilingualism. This approach considers the social construction of bi/multilingualism and the everyday practices of multilinguals in diverse contexts, in relation to policy, theory, and professional practice. With the growing number of multilingual students from diverse backgrounds participating in FSL teacher and language education programs, there is a critical need to (re)shape pedagogies that reflect the complex linguistic repertoires and social practices of youth with multiple, heterogeneous identities in today’s classrooms: diversity within Canada’s linguistic duality. From this vein, the author argues for a multidimensional, reflexive, and interdisciplinary approach for FLP and official bilingual education: one that values heterogeneity; as well as fosters a deeper engagement with the teaching (and learning) of languages, namely French.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".