Preparing Second Language Teachers in Quebec: Responding to Local and Global Realities in the Greek Community
Bibliographic record
Abstract
This presentation reports on a developing case study of teacher education programdesign, revisioning and relevance. It also reports on a unique opportunity tocontribute in positive and tangible ways to language maintenance in a minoritylanguage community. We describe the process of responding to a local Greekcommunity’s appeal to prepare teachers of Greek language and culture bycreating a pathway within the confines of our existing teacher educationprograms. This process involved developing a sociolinguistic and historicalunderstanding of the community, performing a needs analysis, going onsite-visits to the target cultural schools, and engaging in cross-facultycollaboration in mapping out and developing a suitable program. This presentation should be of interest toteacher educators, program directors and applied linguists in considering howto maintain the relevance of our programs in preparing teachers for localcommunities’ and society’s 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".