Multilingual Learners in Canadian French Immersion Programs: Looking Back and Moving Forward
Bibliographic record
Abstract
French immersion (FI) programs in Canada have historically served predominantly Canadian-born, English-speaking students and families in their endeavour to learn both of the country’s official languages, French and English. However, FI programs are becoming increasingly culturally and linguistically diverse as a result of increased global migration to Canada, and many newcomer, multilingual families are interested in providing official-language bilingual education opportunities for their children. The present article is a hybrid literature review and reflection article pertaining to multilingual learners in FI programs. The first section presents a synthesis of post-millennial research (2000-present) according to the following four areas of inquiry: (a) language education policy, (b) educator perspectives, (c) motivation, and (d) achievement. Subsequently, the second section introduces two emerging areas for future research: (a) intersections of race, migration, and language; and (b) plurilingual education. The article seeks not only to summarize recent research with respect to multilingual learners in FI programs but also to set the stage for important and timely areas of future research and to promote more inclusive and equitable FI programs in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".