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Record W4377240210 · doi:10.1017/s1366728923000287

Mixed-language input and infant volubility: Friend or foe?

2023· article· en· W4377240210 on OpenAlexafffund
Yufang Ruan, Krista Byers‐Heinlein, Adriel John Orena, Linda Polka

Bibliographic record

VenueBilingualism Language and Cognition · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia UniversityUniversity of British ColumbiaMcGill UniversityCentre for Research on Brain Language and Music
FundersSocial Sciences and Humanities Research Council of CanadaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentFonds de Recherche du Québec-Société et CultureNational Institutes of HealthChina Scholarship CouncilConcordia UniversityMcGill UniversityCentre for Research on Brain, Language and MusicFoundation for the National Institutes of Health
KeywordsPsychologyLanguage developmentDevelopmental psychologyMixing (physics)Feature (linguistics)Point (geometry)Infant developmentLinguisticsCommunicationMathematics

Abstract

fetched live from OpenAlex

Language mixing is a common feature of many bilingually-raised children's input. Yet how it is related to their language development remains an open question. The current study investigated mixed-language input indexed by observed (30-second segment) counts and proportions in day-long recordings as well as parent-reported scores, in relation to infant vocal activeness (i.e., volubility) when infants were 10 and 18 months old. Results suggested infants who received a higher score or proportion of mixed input in one-on-one social contexts were less voluble. However, within contexts involving language mixing, infants who heard more words were also the ones who produced more vocalizations. These divergent associations between mixed input and infant vocal development point for a need to better understand the causal factors that drive these associations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
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 science0.0000.000
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 head0.029
GPT teacher head0.317
Teacher spread0.288 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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