Supporting Students with Exceptionalities in French Immersion Programs in Ontario
Bibliographic record
Abstract
French Immersion (FI) education continues to be a popular choice for parents across Canada. However, recently FI programs have come under criticism for not being inclusive to students with exceptionalities. While there has been research around the suitability of FI for students and the factors that attribute to students leaving the program, this qualitative research study serves to investigate the perceptions of teachers who are modifying and accommodating for students with exceptionalities in FI classrooms. The methodology of this study was to conduct semi-structured interviews with two Ontario certified teachers who have worked in FI classrooms for at least five years and have experience supporting students with exceptionalities in the FI context. Through the transcription and coding of the interviews, four themes became apparent and led to important implications for FI programs. First, the participants revealed that despite an increase of students who are considered exceptional and that require additional support in FI programs, there continues to be a trend of students with exceptionalities leaving the program. Next, participants aligned with current research around student suitability in FI, stating that students with exceptionalities are no greater risk for success in learning French. Finally, the participants identified current strategies and resources for students with exceptionalities in FI as well as how lack of availability and access creates a barrier for the design and implementation of an equitable program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".