The Role of Environmental Factors on Grammatical Development in French–English Bilinguals Attending a Dual Language Programme in France
Bibliographic record
Abstract
This mixed methods study explores the effects of cumulative exposure, age of onset of acquisition (AO), parent proficiency and richness of the language environment on the grammatical development in French and English of 49 French–English bilingual children who were acquiring the languages either simultaneously (2L1) or successively (cL2). Participants (24 girls) were in 1st grade (M = 6;4, n = 20) or 5th grade (M = 10;4, n = 29), attending a state school in France, in a French–English bilingual programme. Production data come from a narrative task in each language. Parent questionnaires were used to explore environmental factors. Results show, first, that children’s age and parent proficiency were positive predictors of grammatical accuracy in English, while in French only cumulative exposure was a positive predictor. Secondly, exposure showed a stronger relationship with grammatical accuracy in cL2 children; however, only in French, the language in which children made more errors overall. Finally, we found that both 2L1 and cL2 children made gender errors, an early-acquired structure in French. A qualitative analysis of errors with gender highlights, first, the importance of language output for grammatical development, even for children receiving substantial language input and, second, the role of home factors which play a more important role than community language use in shaping grammatical development. This study underscores the complex, interconnected nature of experiential effects on bilingual grammatical development in each language.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".