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Record W2514245350 · doi:10.1017/s1366728916000833

Vocabulary size and speed of word recognition in very young French–English bilinguals: A longitudinal study

2016· article· en· W2514245350 on OpenAlexaff
Jacqueline Legacy, Pascal Zesiger, Margaret Friend, Diane Poulin‐Dubois

Bibliographic record

VenueBilingualism Language and Cognition · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsVocabularyComprehensionVocabulary developmentNeuroscience of multilingualismPsychologyLongitudinal studyWord (group theory)LinguisticsComputer scienceArtificial intelligenceMathematicsStatistics

Abstract

fetched live from OpenAlex

A longitudinal study of lexical development in very young French-English bilinguals is reported. The Computerized Comprehension Test (CCT) was used to directly assess receptive vocabulary and processing efficiency, and parental report (CDI) was used to measure expressive vocabulary in monolingual and bilingual infants at 16 months, and six months later, at 22 months. All infants increased their comprehension and production of words over the six-month period, and bilingual infants acquired approximately as many new words in each of their languages as the monolinguals did. Speed of online word processing was also equivalent in both groups at each wave of data collection, and increased significantly across waves. Importantly, significant relations emerged between language exposure, vocabulary size, and processing speed, with proportion of language exposure predicting vocabulary size at each time point. This study extends previous findings by utilizing a direct measure of receptive vocabulary development and online word processing.

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 score0.020
Threshold uncertainty score0.041

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.300
Teacher spread0.274 · 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

Citations65
Published2016
Admission routes1
Has abstractyes

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Same venueBilingualism Language and CognitionSame topicLanguage Development and DisordersFrench-language works237,207