The relationship between bilingual exposure and morphosyntactic development
Bibliographic record
Abstract
PURPOSE: The study examined the effect of bilingual input on the grammatical development of bilingual children in comparison to monolingual peers. METHOD: Spontaneous language samples were collected in English and French from typically-developing bilingual and monolingual pre-schoolers aged 3 years (n = 56) and 5 years (n = 83). Within each age group, children varied in bilingual exposure patterns but were matched on age, non-verbal cognition, maternal education and language status, speaking two majority languages. Measures included mean length of utterance (MLU) in words and morphemes, and accuracy and diversity of morphological use. RESULT: Grammatical development in each language was strongly influenced by amount of same-language experience. Children with equal exposure to both languages scored comparably to monolingual children in both languages, whereas children with unequal exposure evidenced similarly unequal performance across languages and scored significantly lower than monolinguals in their weaker language. Scoring significantly lower than monolinguals in both languages may, therefore, be a sign of language impairment. Each language followed a strongly language-specific sequence of acquisition and error patterns. Five-year-old children with low exposure to English displayed an optional infinitive pattern, a strong clinical marker for Primary Language Impairment in monolingual English-speaking children. CONCLUSION: Descriptive normative data are presented that permit more accurate interpretation of bilingual assessment data.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".