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Record W4400881279 · doi:10.17759/jmfp.2024130205

Bilingual Language Experience and Executive Functions: Current State of Research

2024· article· en· W4400881279 on OpenAlexaff
Елена Семенова

Bibliographic record

VenueJournal of Modern Foreign Psychology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyExecutive functionsCurrent (fluid)State (computer science)Cognitive psychologyCognitionNeuroscienceProgramming languageComputer science

Abstract

fetched live from OpenAlex

Bilingualism has been claimed to enhance executive functions, as evidenced by studies comparing bilinguals and monolinguals across various executive function tasks. Despite this assertion, recent systematic reviews and meta-analyses reveal contradictory findings, challenging the existence of the “bilingual advantage” phenomenon. The variability in results is attributed not to the data obtained but rather to the diverse methodological paradigms utilized across studies. This article analyzes the operationalization of bilingualism and executive functions in contemporary research alongside a critical examination of the theoretical underpinnings linking bilingualism and executive functions. The paper concludes by outlining avenues for future research, emphasizing the necessity for researchers to account for individual bilingualism-related factors and specifics of the executive function tasks. Furthermore, it advocates for the development of new falsifiable theories that suggest testable hypotheses, thus advancing the field's understanding of bilinguals’ executive functioning.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.113
GPT teacher head0.462
Teacher spread0.348 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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