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Record W2821210002 · doi:10.1017/s1366728918000160

Early executive function: The influence of culture and bilingualism

2018· article· en· W2821210002 on OpenAlexaff
Crystal D. Tran, Maria M. Arredondo, Hanako Yoshida

Bibliographic record

VenueBilingualism Language and Cognition · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsNeuroscience of multilingualismPsychologyCognitionFunction (biology)Developmental psychologySet (abstract data type)Executive functionsCognitive psychologyNeuroscienceBiology

Abstract

fetched live from OpenAlex

Evidence suggests that cultural experiences and learning multiple languages have measurable effects on children's cognitive development (EF). However, the precise impact of how bilingualism and culture contribute to observed effects remains inconclusive. The present study aims to investigate how these factors shape the development of early EF constructs longitudinally, between monolingual and bilingual children at ages 3, 3 ½ and 4 years, with a set of EF tasks that are uniquely relevant to the effects of bilingualism and cultural practices. We hypothesize that the effects of bilingualism and cultural backgrounds (i.e., Eastern) are based on different, though related, cognitive control processes associated with different EF constructs. Results revealed a significant bilingualism effect on cognitive control processes measuring selective attention, switching, and inhibition; while an effect of culture was most pronounced on behavioral regulation/response inhibition. Contributions of bilingualism and cultural experiences on individual EF constructs across development are discussed.

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)0.002
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.011
GPT teacher head0.293
Teacher spread0.282 · 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

Citations80
Published2018
Admission routes1
Has abstractyes

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