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Record W4302169248 · doi:10.1558/jmbs.19494

Native language development of Dutch–English bilingual children in Australia

2022· article· en· W4302169248 on OpenAlexaboutno aff
Marrit Janabi, Elisabeth Duursma, Margot I. Visser‐Bochane, Stefani Ribeiro Knijnik, Hans Bogaardt

Bibliographic record

VenueJournal of Monolingual and Bilingual Speech · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsFirst languageLanguage developmentLanguage assessmentPsychologyReading (process)Second-language attritionComprehension approachTest (biology)English languageLinguisticsLanguage educationDevelopmental psychologyMathematics education

Abstract

fetched live from OpenAlex

This study aimed to track language development and possible factors of language loss in 50 primary-school-aged bilingual Dutch–English children, and it follows up a study conducted one year prior. Dutch language skills were assessed through the standardized language test CELF4-NL and language background factors were assessed through the Alberta Language Environment Questionnaire. Reading books in the native language Dutch contributed significantly to children's language development. Speaking the native language at home with both parents and siblings contributed to better Dutch language skills. Additionally, schooling outside of the home situation in the native language seems to contribute to positive language development in children after one-year follow up.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.117
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.052
GPT teacher head0.421
Teacher spread0.370 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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