Trilingual families’ language strategies: potential predictors and effect on trilingual exposure
Bibliographic record
Abstract
Family language strategies are approaches that parents adopt for language use with their multilingual children. In bilingual contexts, these strategies influence children's language exposure and development (Macleod et al., 2022). In the more complex context of trilingualism, how families settle on strategies and their relationship with exposure may differ. We examined these relationships in a pre-registered online study of 31 families raising trilingual toddlers aged 18-36 months living in Montreal with English, French - the city's two community languages - and various heritage languages. Families' language strategy and language background, children's exposure, and parents' attitudes and concerns towards children's trilingualism were assessed via questionnaire. The most frequent strategies adopted involved mixed use of a community and heritage language with children. Strategies that excluded the community languages at home were associated with lower parent proficiency in the community languages and higher heritage language exposure. Mixed strategies led to more balanced exposure to the three languages. Attitudes towards trilingualism were favorable, concerns were weak, and neither showed a relationship with family language strategy choice. These findings shed new light on unique features of trilingual language environments and open future directions for research on how they relate to the development of three languages.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".