The impact of bilingual language learning on whole-word complexity and segmental accuracy among children aged 18 and 36 months
Bibliographic record
Abstract
This study investigates the phonological acquisition of 19 monolingual English children and 21 English?French bilingual children at 18 and 36 months. It contributes to the understanding of age-related changes to phonological complexity and to differences due to bilingual language development. In addition, preliminary normative data is presented for English children and English?French bilingual children. Five measures were targeted to represent a range of indices of phonological development: the phonological mean length of utterance (pMLU) of the adult target, the pMLU produced by the child, the proportion of whole-word proximity (PWP), proportion of consonants correct (PCC), and proportion of whole words correct (PWC). The measures of children's productions showed improvements from 18 to 36 months; however, the rate of change varied across the measures, with PWP improving faster, then PCC, and finally PWC. The results indicated that bilingual children can keep pace with their monolingual peers at both 18 months and 36 months of age, at least in their dominant language. Based on these findings, discrepancies with monolingual phonological development that one might observe in a bilingual child's non-dominant language could be explained by reduced exposure to the language rather than a general slower acquisition of phonology.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".