Expanding Participation in Hebrew Education: Lessons Learned from French Immersion
Bibliographic record
Abstract
This article explores ideas for innovating the field of Hebrew education (HE) in non-Orthodox day schools in diaspora communities. It proposes strategies and identifies areas of further research to expand the reach of HE schooling to attract new families. These strategies were created through defining and exploring the Toronto HE landscape, and then comparing between HE and French Immersion (FI) programs in Ontario, where the performance and benefits of the latter are found to help inform meaningful reforms in the former. Interviews were subsequently conducted with affiliates of different schools within the FI system in Toronto. Jewish parents were asked about the factors that drew them to register their children in an FI program. Along with an FI teacher and principal, they were also asked to identify successes and challenges of FI practices. Finally, the paper discusses suggestions for future studies and offers ideas to help expand participation in HE and further strengthen the connection of diaspora communities to the Hebrew language.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".