Teaching Canada's official languages: Issues of and linguistic and professional identity
Bibliographic record
Abstract
As an officially bilingual country, learning English or French as a second language is part of the Canadian experience. Accordingly, both native and non-native speakers of English and French may aspire to be English as a Second Language (ESL) or French as a Second Language (FSL) teachers. A better understanding of how to employ this reality in pre-service second language (L2) teacher education programs is significant for both the preparation of effective language teachers, as well as the learning outcomes and French/English attainment of their students. This research investigates the emerging professional and linguistic identities of native and non-native English and French speaking pre-service teachers in a Teaching ESL (TESL) program in Quebec, and a Teaching FSL (TFSL) program in Ontario. The phenomenon is explored through qualitative surveys, one-on-one semi-structured interviews with pre-service teachers, and focus groups with teacher educators, with a view to exploring the relationship between language identity and teacher professional identity, and how this contributes to effective L2 teaching practice and L2 learning. This presentation reports on the preliminary results of this investigation, exploring notions such as self-assessed L2 proficiency, cultural understandings, linguistic and professional identity, and social belonging to L1 and L2 communities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".