Principals’ Beliefs About Language Learning and Inclusion of English Language Learners in Canadian Elementary French Immersion Programs
Bibliographic record
Abstract
Gatekeepers’ language ideologies and beliefs about language learning determine how learners experience French as a second language programs, in particular, their access to, and success and inclusion in, the French immersion (FI) program. In this article, we explore how FI principals understand language learning and the inclusion of English language learners (ELLs) and how these perspectives shape school policy in FI programs. The study took place in a large urban school board in southern Ontario with student population consisting of 50% ELLs. After conducting a questionnaire with principals across the school board, we interviewed a subgroup of principals gathering quantitative and qualitative data. In addition, we consulted the board’s website and documentation provided to parents regarding enrolling their children in FI. The study highlights convergence or divergence from principals’ beliefs in relation to board policy about access and inclusion to the FI program. Through critical discourse analysis, data revealed that principals have contradictory beliefs about language learning, and at times principals struggle to reconcile these beliefs with official board policy. While FI principals are mostly positive about including ELLs in FI, to provide equal access to the program, they would benefit from (a) moving away from a definition of bilingualism as equalingualism and (b) expanding and developing the meaning of inclusivity beyond physical presence to adapt the FI space for greater inclusion of ELLs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".