Exemption and Exclusion from French Second Language Programs in Canada: Consideration of Novice Teachers’ Rationales
Bibliographic record
Abstract
This case study is focused on a small group of novice teachers of French as a second language (FSL) in the Canadian K–12 context. More specifically, it presents the perceptions and ideas that inform new teachers’ views toward the suitability of French as a second language and toward exemption and/or exclusion for two populations: students who are English language learners (ELLs) and students with learning difficulties and other special needs. The data from the current study are drawn from semi-structured interviews implemented over the first four years of a larger five-year study. The findings reveal that there was general openness to the idea of including students who are ELLs and who have learning difficulties in FSL programs. However, in some instances the participants viewed exemption as a reasonable path for the student population when, in isolated ways, the program was considered as unsuitable for their needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".