Multilingualism and literacy development in interlingual families
Bibliographic record
Abstract
Heritage languages are key to shaping the identity of many individuals who grow up in environments where the dominant societal language is different from their home languages. Yet heritage language learners can be incredibly diverse in terms of cultural and language backgrounds, language proficiency, literacy skills, language socialization experiences and in many other ways. Heritage language education and literacy development, in particular, have been examined in both formal and community-based educational settings. Insights drawn from this growing area of research have informed our understanding of challenges faced by heritage language learners in relation to literacy socialization, such as a lack of educational resources and community support. A subset of this research examines the issues faced by mixed-heritage language families in relation to literacy. This article reports on the qualitative phase of a mixed-method study on the language and literacy socialization experiences of interlingual families in Canada with mothers of Japanese descent. The findings highlight the multiple challenges faced by the participants in relation to the development of Japanese literacy. It draws attention to the complexity of their family lives, and how the promotion of multilingualism in the two official languages of Canada comes at the expense of Japanese literacy skills for their children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".