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Record W4310230603 · doi:10.3390/su142315788

Home Literacy Environment and Chinese-Canadian First Graders’ Bilingual Vocabulary Profiles: A Mixed Methods Analysis

2022· article· en· W4310230603 on OpenAlexafffundabout
Guofang Li, Zhuo Sun, Fubiao Zhen, Xuejun Ryan Ji, Lee Gunderson

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHeritage languageNeuroscience of multilingualismVocabularyPsychologyContext (archaeology)LiteracyFirst languageDevelopmental psychologyThematic analysisMultimethodologyVocabulary developmentLinguisticsQualitative researchMathematics educationPedagogySociologyGeographyTeaching method

Abstract

fetched live from OpenAlex

Bilingual children in the North American context significantly improve in English language proficiency, but their heritage language learning varies between different linguistic groups. This mixed methods study was designed to explore the developmental patterns in bilingual vocabulary among Chinese-Canadian first-graders’ (N = 75) and to identify home factors that may have contributed to divergent bilingual developmental trajectories. Cluster analyses were conducted to identify underlying discrepancy profiles in bilingual oral lexicon. Four children with contrasting bilingual profiles were selected for qualitative analysis to explore home factors that may have contributed to the discrepancies. Thematic analyses of parental interviews revealed several family factors such as beliefs and attitudes toward bilingualism, quality literacy engagement, and sibling dynamics, that all appearing to contribute to the discrepancies.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0040.001
Scholarly communication0.0030.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.323
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2022
Admission routes3
Has abstractyes

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