Inclusionary Practices in French Immersion: A Need to Link Research to Practice
Bibliographic record
Abstract
French immersion programs were introduced in Canada over forty years ago. Since then, questions have been raised regarding the suitability of French immersion for students with academic and learning difficulties. Research examining the suitability of French immersion for a wide range of learners is highlighted in this article, in order to make links between research and the inclusionary practices that are currently being promoted in French immersion programs. An analysis of the extent to which different Canadian jurisdictions address key concepts related to inclusion in French immersion, including accessibility and retention, is then presented. Current gaps in the ways in which inclusionary practices in French immersion are supported are described in the final sections of this article, with the hope of advancing the promotion of early French immersion as a fully inclusionary program option.
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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 | 0.157 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.022 | 0.061 |
| Scholarly communication | 0.037 | 0.026 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".