Toward a policy of heterogeneity in Canada: The journeys of integration of multilingual student teachers of FSL in Ontario in plurilingual times
Bibliographic record
Abstract
This article details the critical need for a policy of heterogeneity in Canada. Canada is changing, with a growing number of youth with multiple, overlapping and complex identities and linguistic repertoires. From this end, I argue that we can no longer look at language, identity,or community as separate, static or fixed categories and in this vein, we need official and public policies that support linguistic diversity and value heterogeneity. Drawing upon my ethnographic and sociolinguistic research which investigates multilingual youth training to become teachers of French as a Second Language (FSL) in Ontario, I demonstrate the importance of sociocultural research as regards multilingualism for language planning and policy, particularly when it comes to creating policies that reflect people’s use of language(s) rather than simply seeing people as language users. In my fieldwork, I have found that the impact of multilingual practices tend to blur traditional boundaries related to languages, identities, cultures and education. This article contributes to language policy and planning as it aims to put forth new ways of conceptualizing multilingualism in relation to the development of theory, policies and professional practice in the fields of language education, teaching and public policy.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.082 | 0.017 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".