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Record W3192273339 · doi:10.1075/sibil.57.20nad

Proficient bilingualism may alleviate some executive function difficulties in children with Autism Spectrum Disorders

2019· book-chapter· en· W3192273339 on OpenAlexaff
Aparna Nadig, Ana Maria Gonzalez‐Barrero

Bibliographic record

VenueStudies in bilingualism · 2019
Typebook-chapter
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsAutismPsychologyNeuroscience of multilingualismDevelopmental psychologyExecutive functionsSpectrum (functional analysis)Function (biology)Cognitive psychologyAudiologyCognitionMedicineNeurosciencePhysicsBiologyEvolutionary biology

Abstract

fetched live from OpenAlex

Abstract A bilingual advantage, or enhanced performance on executive function (EF) tasks, has been identified in typically-developing bilingual children relative to monolinguals. Children with Autism Spectrum Disorders (ASD) demonstrate significant EF difficulties in comparison to typically-developing peers. Perhaps bilingualism could alleviate EF impairments in ASD? We review our lab’s findings from bilingual vs. monolingual school-age children with ASD, and those with typical development, on both performance tasks and parent ratings of EF application in daily life. We present the first evidence of a bilingual advantage in ASD on EF performance tasks (verbal fluency and dimensional change card sort), but not parent ratings. The implications of these preliminary findings for future research and clinical practice with children with ASD and other neurodevelopmental disorders 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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.313
Teacher spread0.275 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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