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Record W3009087940 · doi:10.1017/s030500092000077x

Multilingual toddlers’ vocabulary development in two languages: Comparing bilinguals and trilinguals

2021· article· en· W3009087940 on OpenAlexaff

Bibliographic record

VenueJournal of Child Language · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia UniversityCentre for Research on Brain Language and Music
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsVocabularyVocabulary developmentNeuroscience of multilingualismAffect (linguistics)Language acquisitionLanguage development

Abstract

fetched live from OpenAlex

Many children grow up hearing multiple languages, learning words in each. How does the number of languages being learned affect multilinguals' vocabulary development? In a pre-registered study, we compared productive vocabularies of bilingual (n = 170) and trilingual (n = 20) toddlers aged 17-33 months growing up in a bilingual community where both French and English are spoken. We hypothesized that because trilinguals have reduced input in French and English due to time spent hearing their third language, they would have smaller French-English vocabulary sizes than bilinguals. Trilinguals produced on average 2/3 of the number of words in these languages that bilinguals did: however, this difference was not statistically robust due to large levels of variability. Follow-up analyses did, however, indicate a relationship between input quantity and vocabulary size. Our results indicate that similar factors contribute to vocabulary development across toddlers regardless of the number of languages being acquired.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.345
Teacher spread0.327 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2021
Admission routes1
Has abstractyes

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