No ESL in English Schools: Language Policy in Quebec and Implications for TESL Teacher Education
Bibliographic record
Abstract
In this article, various aspects of official language policy in Quebec are seen as interacting with contested and contesting ideologies, as experienced by novice teachers in teaching English as a second or other language within the majority French school system. The context of TESL training in Quebec is described, focusing on legislative policy and the status of English in schools. This is followed by a discussion of problems encountered in the current educational context by students in a bachelor's of education in TESL program at an English university in Montreal, Quebec, Canada. Several concerns were identified through student responses to a questionnaire and from reflection on their student‐teaching experiences. The resulting areas discussed here are the consequences of low student‐teacher language proficiency (English and French); ambivalent or hostile attitudes toward English or English as a second language (ESL) on the part of students and teachers in schools; (non)use of English in the ESL classroom; low motivation of ESL students; and the nature of English language and culture in Quebec. Although none of these is unique to this context, the particular circumstances, history, and ideologies in this context are major factors in the government language policy, popular language attitudes, and second language education practice. For each area of concern, specific ways in which the teacher education program addresses these concerns are described, including excerpts from a student teacher‐to‐student teacher advice handbook.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".