Canadian Bilingual Program Teachers’ Understanding of Immersion Pedagogy: A Nexus Analysis of an Early Years Classroom
Bibliographic record
Abstract
Bilingual language education in Canada comprises Bilingual Programs in minority languages and programs in official languages (e.g., French immersion). However, pedagogy in Bilingual Programs has not been studied to the same depth as it has been in French Immersion, so little is known about the teaching practices within them. Immersion pedagogy, the integration of content and language education, fosters teaching practices that encourage language development but sometimes also monolingual instructional practices that discourage the use of flexible bilingualism strategies. This research in an early years German Bilingual Program classroom examines what teachers understand as “immersion pedagogy.” This nexus analysis draws on data from curriculum documents, classroom observation, interviews, and stimulated recall sessions. The rationale for this study is to address the lack of studies examining teaching practices within Bilingual Programs as a unique form of bilingual education. The findings reveal teachers’ understanding of immersion pedagogy as content- and language-integrated instruction with strong discourses in place that include the separation of languages. The conclusions shed light on the need for teachers to further explore their understanding of immersion pedagogy and identify potential strategies and innovations to explore in-school professional learning communities (e.g., flexible bilingual strategies).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".