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Record W2949041551 · doi:10.1017/s1366728918001207

Are there cognitive benefits of code-switching in bilingual children? A longitudinal study

2019· article· en· W2949041551 on OpenAlexaff
Olivia Kuzyk, Margaret Friend, Vivianne Severdija, Pascal Zesiger, Diane Poulin‐Dubois

Bibliographic record

VenueBilingualism Language and Cognition · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsCode-switchingContext (archaeology)PsychologyCognitionTypologyCode (set theory)Developmental psychologyExecutive functionsLongitudinal studyComputer scienceLinguisticsProgramming languageSociologyHistoryMathematicsStatistics

Abstract

fetched live from OpenAlex

The current study explored bilingual parent and child code-switching patterns over time. Concurrent and predictive models of code-switching behaviour on executive function outcomes were also examined in a sample of 29 French-English bilinguals at 36 (Wave 1) and 61 (Wave 2) months of age. We investigated whether code-switching typology in a single-language context predicted executive function performance at each wave independently, and whether growth in code-switching frequency across waves predicted executive function performance at Wave 2. At both waves, parents and children participated in two free play sessions (in English and French), followed by a battery of executive function tasks administered in the dominant language. Results indicate more frequent code-switching from the non-dominant to the dominant language in children, and that children code-switch to fill lexical gaps. Results also suggest that less frequent code-switching in a single-language context is associated with better inhibitory control skills during the preschool period.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.026
GPT teacher head0.318
Teacher spread0.292 · 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

Citations59
Published2019
Admission routes1
Has abstractyes

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