Relations between phonological production, grammar and the lexicon in bilingual French-English children
Bibliographic record
Abstract
AIMS AND OBJECTIVES: This study examines multiple associations between language domains in bilingual children with a focus on phonology. Previous studies indicate within- but not cross-language associations between vocabulary and grammar in bilingual children. We investigate whether the relation between phonology and other language domains differs from the one reported between vocabulary and grammar. METHODOLOGY: = 31), aged 31 months, participated in 2 free-play sessions, from which lexical, grammatical and phonological information was extracted. The children's parents completed the MacArthur-Bates Communicative Developmental Inventories and its Canadian French adaptation providing additional information on vocabulary and grammar in each of the children's languages. They also completed a questionnaire on their children's exposure to French and English. DATA ANALYSIS: Within and cross-language relations between phonology, vocabulary and grammar were investigated using correlational analyses and mixed logistic regression. FINDINGS: Correlational analyses did not reveal significant cross-language relations between phonology, vocabulary and grammar. However, mixed logistic regression, which controlled for language exposure effects, indicated that phonology was influenced by vocabulary and grammar both within and across languages. ORIGINALITY: This study is one of the first to study cross-domain relations involving phonology in young bilingual children. IMPLICATIONS: Overall, the findings suggest that phonology displays a pattern of relations that is different from other language domains engendering between-language effects due to a language-general component.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".