Complex sentence production in bilingual and monolingual children
Bibliographic record
Abstract
Bilingual children often lag behind monolinguals on standardized measures of language acquisition, such as vocabulary tests. This bilingual lag could be related bilinguals’ lesser experience with the target language relative to monolinguals. In this study, we predicted that sequential Mandarin-English bilinguals would perform worse than same-aged English monolinguals on a standardized measure of complex sentence production. As predicted, the bilingual preschoolers performed worse than age-matched English monolinguals. However, once English experience was covaried, there was no significant difference between the two groups. After controlling for age, we tested three predictors of complex sentence production: (1) English vocabulary, (2) verbal memory, and (for the bilinguals) (3) Mandarin vocabulary. For both bilinguals and monolinguals, English vocabulary and verbal memory were significant predictors. These results support the argument that experience with a particular language is highly predictive of children’s ability to produce complex sentences in that language. Verbal memory is also an important predictor of individual differences in the ability to produce complex sentences.
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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) | 0.001 | 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) | 0.003 | 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".