Within- and Cross-Language Relations Between Phonological Memory, Vocabulary, and Grammar in Bilingual Children
Bibliographic record
Abstract
PURPOSE: = 25) bilingual children at 30, 36, and 48 months. It was predicted that phonological memory would display both within-language and between-language relations to language development and that these relations would be stronger at the youngest age. METHOD: Bilingual children participated in free-play sessions in both of their languages at each age, from which vocabulary and grammatical information (number of different words and mean length of utterance) was extracted. Vocabulary information was also obtained from parent inventories completed when the children were 30 months and a standardized receptive vocabulary test administered at 36 and 48 months. The children were also administered nonword repetition tests in both of their languages at each age. RESULTS: Mixed logistic regression indicated that phonological memory was associated with vocabulary and grammar within the same language and phonological memory in the other language. In two of the four statistical models, phonological memory exhibited positive between-language relations, and in one model, it exhibited negative between-language relations to language development. Results also indicated that within-language and between-languages effects remained constant, or between-language associations decreased during the age range studied. CONCLUSION: Overall, the findings provide some support for cross-language associations between phonological memory and lexical and grammatical skills.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".