An Investigation into Public and Community Heritage Language Programming in Canada
Bibliographic record
Abstract
Immigrant children who speak languages other than English or French as a first language can learn their heritage languages (HLs) at public and community schools in Canada. HLs refer to immigrants' first (parental) languages and in cases where immigrants do not speak their parental languages, HLs include their ancestors' first languages. HL programs play an important role in immigrant children's language maintenance, which refers to the ability to use or continue using their HLs. However, these programs face potential challenges such as a lack of funding (Cummins 2005) and well-developed instructional materials (Feuerverger 1997). In this paper, first, the importance of HL maintenance will be overviewed, followed by a discussion of HL programming in Canadian public schools. Second, HL programming at community schools are overviewed, followed by the challenges faced by a community school in a major Canadian city. Conclusion and recommendations for improving HL programs are presented in the end.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".