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Record W4415756196 · doi:10.1080/10489223.2025.2534392

Does pointing predict bilingual children’s vocabulary?

2025· article· en· W4415756196 on OpenAlexafffund
Elena Nicoladis, Alexandre J. S. Morin, Diane Poulin‐Dubois

Bibliographic record

VenueLanguage Acquisition · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia UniversityUniversity of British Columbia
FundersNational Institute of Child Health and Human DevelopmentNatural Sciences and Engineering Research Council of Canada
KeywordsLanguage proficiencyLanguage acquisitionNeuroscience of multilingualismTheoretical linguisticsComprehension approachMorphemeFirst languageLanguage assessmentPragmatics

Abstract

fetched live from OpenAlex

Previous studies have shown that children’s and parents’ pointing predicts monolingual children’s vocabulary, both concurrently and longitudinally. In this study, we predicted that children’s and parents’ pointing would positively predict bilingual children’s vocabulary growth in both languages. However, the strength of that link could differ depending on the child’s dominance, as parents often play a more didactic role when interacting in children’s non-dominant language. Children’s pointing might be a stronger predictor of their dominant language vocabulary. Parents’ pointing might be a stronger predictor of their non-dominant language vocabulary. Participants were 35 French-English bilingual children observed in free play situations, one in each language, at 30 months. Their vocabulary scores were collected at 30, 37, 49, and 63 months. As expected, children’s pointing predicted growth in dominant language vocabulary and parents’ pointing growth in children’s non-dominant language vocabulary. We discuss how parents’ interactional roles mediate how pointing relates to vocabulary growth.

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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.269
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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