Elitist, Inequitable and Exclusionary Practices: A Problem within Ontario French Immersion Programs? A Literature Review
Bibliographic record
Abstract
The 2013 Ontario French Second Language (FSL) Curriculum emphasizes inclusivity and bilingualism; however, many students are recommended to opt out of French Immersion (FI). The opting-out of students may support the strengthening of the program by establishing a reputation of success, but how does it affect the withdrawn child? Are FSL programs using best practices to support all learners equitably, or catering to the elite students as a result of misconceptions, lack of resources and professional training? To address these questions, an exploratory and focused literature review of Canadian publications, Ministry of Education documentations and global articles on the topic of bilingualism was conducted, focusing on the works of Genesee (2007) and Baker (2006) on natural language acquisition, Arnett and Mady (2017) on teachers’ and parents’ perspectives, and Gour (2015) and Wise (2012) who report on misconceptions regarding second language education. Emerging trends indicate that elitist practices and unequal access to FSL programs remain a prominent issue in Ontario classrooms. With the understanding that students with learning disabilities (LDs) can succeed in the FI program, removing these learners may in turn, be a disservice to their overall learning. Findings presented in this paper support the need to examine how learners’ abilities are being perceived by educational professionals to provide the necessary tools and supports for success, appropriate training to mitigate misconceptions, as well as retain a reputation for success in FSL programs through equitable means. Acknowledging such discrepancies between what serves as best teaching practice and making it possible in the classroom is necessary to reduce excuses of unpreparedness to meet students’ diverse needs and initiate reflection and training programs that prepare teachers to teach inclusively to all.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".