The Inclusion of Students with Special Education Needs in French Immersion Programs in Ontario Schools
Bibliographic record
Abstract
From its development in 1965, French Immersion programs have continuously evolved to help meet the needs of the students they serve (Burns, 1986; Guenot & Jaber, 2022). One area, however, that has not received the attention it needs, are programs to support students with special educational needs in French Immersion schools (Mady & Muhling, 2017). In 2015, The Ontario Ministry of Education published the document “Including Students with Special Education Needs in French As A Second Language Programs: A guide for Ontario schools”, to provide insight into supporting students with Special Educational Needs (SEN) in French programs across Ontario. This study will investigate what recent research indicates about the impact of Ontario's 2015 Including Students document on inclusion in special education and the reduction of barriers to access of French Immersion (FI) programs across the province of Ontario for students with special educational needs, through a Systematic Literature Review (SLR) policy analysis. Using the analysis of literature prior to the release of the document and after its release, it was found that although more research literature is aware of the issues facing students with SEN in French Immersion after the release of the document, there are still many barriers facing students with SEN in immersion. Based on the findings from this research, it is pertinent that the Ontario Ministry of Education provide more funding and direction for school boards when it comes to special education programs in French Immersion Environments.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".