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Record W1957189283 · doi:10.1017/s0305000915000252

Vocabulary size, translation equivalents, and efficiency in word recognition in very young bilinguals

2015· article· en· W1957189283 on OpenAlexaff
Jacqueline Legacy, Pascal Zesiger, Margaret Friend, Diane Poulin‐Dubois

Bibliographic record

VenueJournal of Child Language · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsVocabularyPsychologyComprehensionVocabulary developmentNeuroscience of multilingualismLinguisticsTask (project management)Language developmentDevelopmental psychology

Abstract

fetched live from OpenAlex

The present study examined early vocabulary development in fifty-nine French monolingual and fifty French-English bilingual infants (1;4-1;6). Vocabulary comprehension was assessed using both parental report (MacArthur-Bates Communicative Development Inventory; CDI) and the Computerized Comprehension Task (CCT). When assessing receptive vocabulary development using parental report, the bilinguals knew more words in their L1 versus their L2. However, young bilinguals were as accurate in L1 as they were in L2 on the CCT, and exhibited no difference in speed of word comprehension across languages. The proportion of translation equivalents in comprehension varied widely within this sample of young bilinguals and was linked to both measures of vocabulary size but not to speed of word retrieval or exposure to L2. Interestingly, the monolinguals outperformed the bilinguals with respect to accuracy but not reaction time in their L1 and L2. These results highlight the importance of using multiple measures to assess early vocabulary development.

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)0.003
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0010.000

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 head0.027
GPT teacher head0.305
Teacher spread0.277 · 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

Citations93
Published2015
Admission routes1
Has abstractyes

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