“I wouldn't want to impose!” Intercultural mediation in French immersion
Bibliographic record
Abstract
This article examines four French immersion (FI) teachers’ perspectives on the relationship between language and culture and on their roles as intercultural mediators in New Brunswick (NB), Canada's only officially bilingual province. Data were drawn from semi‐structured interviews with the teachers. Couched in the broader framework of Canada's multicultural policy, the cultural outcomes for NB's FI program underscore the importance of learning French in a multicultural and multilingual society. Understanding the relationship between language and culture is part of this; however, the department explicitly states that it does not intend for immersion students to “adopt” Francophone culture. This analysis shows that these teachers employ a variety of language ideologies, all of which culminate in a “language‐as‐code” approach, devoid of culture and removed from its social contexts of use. They do not consider deep cultural discussions to have a place in their classrooms, although each teacher demonstrates some level of intercultural awareness regarding the language/culture dialectic in their private lives. The works by Kohler ( ), Di Stefano ( ), and Kearney ( ) are employed to consider possible ways forward for teacher training on intercultural mediation in immersion classrooms and on curriculum development to target students’ intercultural competence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".