Extended French vs. French Immersion
Bibliographic record
Abstract
A school board in southern Ontario is in the process of phasing out their Extended French program and replacing it with French Immersion at the high school level. In this paper I asked a high school administrator and a French as a second language teacher(s) of this school board to describe the phase out process and to relay any implications they believe that this phase out has had on French as a second language teaching and learning in the school board. I also questioned how French as a second language teachers’ levels of self-efficacy were affected by this program change. This paper found that the replacement of Extended French with French Immersion in the research site has impacted French teaching and learning. Although the French Immersion program is less accessible to students in the school board, participants of this study believe that this program change is a good thing and will positively impact French as a second language teaching and learning in the county. In addition, participants believe that the program change could act as a retention factor for French teachers if they are properly supported during the transition. This paper is extremely relevant given the context of the French as a second language teacher shortage in Ontario. Participants of this study made it very clear that they are in dire need of qualified French as a second language teachers in order to be able to make this program change work. The replacement of Extended French with French Immersion, a program that requires more instruction in French, and therefore more qualified French instructors, will be a challenge given this teacher shortage in Ontario.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".