Biliteracy and trilingual practices in the home context: Case studies of Chinese-Canadian children
Bibliographic record
Abstract
Although Chinese has become the third largest mother tongue in both Canada and the USA, Chinese/English biliteracy development has received little attention in educational research.This article explores three Chinese-Canadian first and second graders’ biliteracy (Chinese/English reading and writing) and trilingual (Mandarin, Cantonese, and English) practices in the home milieu. Findings suggest that the home context is a crucial environment for success or failure in achieving biliteracy. All families expected their children to become biliterate and multilingual, but the three children vary in their preferences and use of different languages and literacies at home. Factors such as parents’ perceptions of their minority status in the host society, their attitudes toward the role of heritage language and their own proficiencies in the dominant language, as well as several school and societal factors, such as quality of instruction in heritage language schools, language policies in the mainstream schools, and the media, played an important role in shaping the children’s language choices and patterns of use at home. These multiple factors suggest that helping immigrant children become biliterate and multilingual is a challenging task that requires concerted efforts between parents, public schools, and community organizations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".