Teachers as Mediators in Language Immersion Education
Bibliographic record
Abstract
This book explores the role of teachers as intercultural mediators within language immersion education programs. The authors draw on research conducted in the context of a one-way French immersion program in New Brunswick, Canada, an officially bilingual province and country. Their discussion is anchored on the landmark Douglas Fir Group framework of second language acquisition, examining the implications of macro-level ideologies for language education, curriculum and intercultural instruction. The book considers educators’ placement within the framework and their potential role as intercultural mediators between macro-level ideologies, meso-level curricular implementation, and their students at the micro level. They even provide an amendment to the framework that models this mediating role. Through interview data with entry point early French immersion teachers and principals of their schools, the authors emphasise the importance of theoretically situating teachers’ positions as mediators of ideology and culture. Through this, we can fully understand what it means to incorporate intercultural competence into language learning. They argue that, teachers receive little support—either through curriculum or through training—on how to engage with (inter)cultural instruction in their practice. They then describe their own course for training pre- and in-service teachers on intercultural mediation in their language education practice, applicable to a variety of language learning models and contexts.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".