Teaching for Transfer: Insights from theory and practices inprimary-level French-second-language classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper illustrates teaching for transfer across languages by synthesizing key insights from theory and previously published research alongside our case study data from primary-level teachers in core French-second-language (CF) classrooms in Ontario, Canada. Drawing on research that redefines language transfer as a resource, this study drew on several influential theoretical notions and data collected through interviews and classroom observations. All of these sources point to a multi-leveled approach to teaching for transfer that includes considerations of learning, teaching and contextual features. Study data suggest that CF teachers plan for transfer and use a range of strategies likely to promote its use with students. This paper connects theory, research and practice with the aim of strengthening dialogue among researchers and educators.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it