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Record W4402884545 · doi:10.1017/s1366728924000439

Cross-linguistic transfer in bilingual children's phonological and morphological awareness skills: a longitudinal perspective

2024· article· en· W4402884545 on OpenAlexaff
Kehui Zhang, Xin Sun, Zahira Flores-Gaona, Chi‐Lin Yu, Rachel L. Eggleston, Nia Nickerson, Valeria C. Caruso, Twila Tardif, Ioulia Kovelman

Bibliographic record

VenueBilingualism Language and Cognition · 2024
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsNeuroscience of multilingualismLinguisticsPsychologyHeritage languageLiteracyPerspective (graphical)VocabularyPhonological awarenessReading (process)Vocabulary developmentPedagogy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.353
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2024
Admission routes1
Has abstractyes

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