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Record W4413062865 · doi:10.1080/13670050.2025.2530213

Home literacy environment and early bilingual vocabulary development among Chinese–Canadian children: a longitudinal study

2025· article· en· W4413062865 on OpenAlexafffundabout
Guofang Li, Fubiao Zhen, Lee Gunderson

Bibliographic record

VenueInternational Journal of Bilingual Education and Bilingualism · 2025
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLiteracyVocabulary developmentNeuroscience of multilingualismVocabularyPsychologyBilingual educationLongitudinal studyDevelopmental psychologyLinguisticsMathematics educationPedagogyTeaching methodMedicine

Abstract

fetched live from OpenAlex

This longitudinal study examines the relationship between Chinese-Canadian children’s (N = 173) home literacy environment (HLE) and their bilingual receptive vocabulary development in English and Chinese from grades 1–3. Bilingual receptive vocabulary in Chinese and English and quality and quantity of HLE measures were collected. Sociodemographic information in L1 (Cantonese or Mandarin), gender, socioeconomic status (SES), and immigration status were also collected. Descriptive analyses of data showed that the children’s English receptive vocabulary increased significantly while their Chinese vocabulary decreased substantially over the three grades. There were also gender, SES, and home language (Mandarin or Cantonese) differences in bilingual receptive vocabulary skills. Hierarchical linear modeling (HLM) analysis results indicated that the quantity of home language use was significantly correlated with Chinese receptive vocabulary development but not with English receptive vocabulary. High-quality home literacy activities were significantly associated with vocabulary in both languages. These findings have important implications for parental engagement in early biliteracy and early literacy instruction in schools.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.337
Teacher spread0.327 · 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.

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

Citations3
Published2025
Admission routes3
Has abstractyes

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