“I’m Trilingual – So What?”: Official French/English Bilingualism, Race, and French Language Teachers’ Linguistic Identities in Canada
Bibliographic record
Abstract
The privileging of French and English in Canada has led to an official language policy that minimizes the country’s long-established multilingual realities in favour of a socio-politically constructed linguistic and cultural duality. The impact of this policy directly shapes the linguistically diverse yet monoglossically constructed French language classroom with teachers overwhelmingly orientated to “balanced” bilingualism. Research narratives generated with Western Canadian French as a second language (FSL) teachers show how an emphasis on official languages constrains the construction of a legitimate professional identity, especially among multilingual teachers of French. The article argues that official French/English bilingualism in Canada constitutes a key obstacle to moving beyond monolingual and racializing language ideologies associated with standardized French and has a significant impact on FSL teacher professional identity. The analysis of interview extracts shows teacher-participants prioritizing English and French at the expense of their heritage (non-official) languages, in effect erasing dimensions of their linguistic, racial, and cultural identities. The article concludes by considering how a lack of researcher reflexivity with regard to issues of race can reinforce both monoglossic ideologies and a well-entrenched disregard for multilingual knowledge in teacher education programs.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".