Cross-linguistic transfer in bilingual children's phonological and morphological awareness skills: a longitudinal perspective
Bibliographic record
Abstract
Abstract Cross-linguistic interactions are the hallmark of bilingual development. Theoretical perspectives highlight the key role of cross-linguistic distances and language structure in literacy development. Despite the strong theoretical assumptions, the impact of such bilingualism factors in heritage-language speakers remains elusive given high variability in children's heritage-language experiences. A longitudinal inquiry of heritage-language learners of structurally distinct languages – Spanish–English and Chinese–English bilinguals ( N = 181, M age = 7.57, measured 1.5 years apart) aimed to fill this gap. Spanish–English bilinguals showed stronger associations between morphological awareness skills across their two languages, across time, likely reflecting cross-linguistic similarities in vocabulary and lexical morphology between Spanish and English. Chinese–English bilinguals, however, showed stronger associations between morphological and word reading skills in English, likely reflecting the critical role of morphology in spoken and written Chinese word structure. The findings inform theories of literacy by uncovering the mechanisms by which bilingualism factors influence child literacy development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".