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Record W4298624655 · doi:10.31234/osf.io/hpwbr

Patterns of language switching and bilingual children’s word learning: An experiment across two communities

2022· preprint· en· W4298624655 on OpenAlexfundaboutno aff
Rachel Ka Ying Tsui, Jessica Elizabeth Kosie, Laia Fibla, Casey Lew‐Williams, Krista Byers‐Heinlein

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthConcordia University
KeywordsVocabularyNeuroscience of multilingualismTask (project management)Word (group theory)Second languagePsychologyLinguisticsFirst languageComputer scienceLanguage acquisitionMathematics education

Abstract

fetched live from OpenAlex

Language switching is common in bilingual environments, including those of many bilingual children. Some bilingual children hear rapid switching that involves immediate translation of words (an ‘immediate-translation’ pattern), while others hear their languages most often in long blocks of a single language (a ‘one-language-at-a-time’ pattern). Our two-site experimental study compared two groups of developing bilinguals from different communities, and investigated whether differences in the timing of language switching impose different demands on bilingual children’ learning of novel nouns in their two languages: do children learn differently if they hear a translation immediately vs. if they hear translations more separated in time? Using an at-home online tablet word learning task, data were collected asynchronously from 3- to 5-year-old bilinguals from French–English bilingual families in Montreal, Canada (N = 31) and Spanish–English bilingual families in New Jersey, USA (N = 22). Results showed that bilingual children in both communities readily learned new words, and their performance was similar across the immediate-translation and one-language-at-a-time conditions. Our findings highlight that different types of bilingual interactions can provide equal learning opportunities for bilingual children’s vocabulary 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

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 science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.027
GPT teacher head0.371
Teacher spread0.344 · 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 designQualitative
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

Citations1
Published2022
Admission routes2
Has abstractyes

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