How community and family support bilingual language development: insights from bilingual Canadian families
Bibliographic record
Abstract
While previous research has shown that language use in the home is an important factor in bilingual language development, little research has focused on how specific language strategies used by parents relate to bilingual children's language exposure and vocabulary development. Yet, for parents, this information has the potential to inform their day-to-day decisions about language use in their home. The present study aims to contribute to this understanding by applying a Bioecological Systems Model to understand how language use within the community and within the family influenced young children’s amount of language exposure and vocabulary abilities. A total of 51 French-English bilingual four-year-old Canadian children participated in the present study. Children’s exposure to each language was measured through parent reports, and their vocabulary was assessed in each language. Differences in community context were not a significant predictor of the amount of exposure or vocabulary abilities. Family language strategies predicted vocabulary scores in English but not French, whereas the amount of exposure predicted vocabulary scores in French but not English. These findings suggest that, in the preschool years, family language use strategies and amount of exposure impact vocabulary development and that their role differs based on context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".