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Record W2545458491 · doi:10.1075/tilar.13.08tho

The typical development of simultaneous bilinguals

2014· book-chapter· en· W2545458491 on OpenAlexaffabout
Elin Thordardottir

Bibliographic record

VenueTrends in language acquisition research · 2014
Typebook-chapter
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrammarRepetition (rhetorical device)VocabularyLinguisticsPsychologyImitationSentenceNeuroscience of multilingualismNormativeLanguage developmentComputer scienceArtificial intelligenceDevelopmental psychology

Abstract

fetched live from OpenAlex

This chapter focuses on the effect of the relative amount of exposure bilingual children have received in each of their languages on their performance on tests of language knowledge (vocabulary and grammar) and language processing (nonword repetition and sentence imitation). Results are reported on studies of two age groups of monolingual and bilingual preschoolers in Montreal, Canada, learning English and French. Amount of input exerted a strong effect on the rate of development of both vocabulary and grammar, but had little impact on children’s ability to repeat nonwords. Children with unequal amounts of exposure to each language had similarly unequal levels of performance both in vocabulary and grammar. Grammatical development followed strongly language specific patterns in terms of order of acquisition, accuracy and error types. Normative data are reported on bilingual children with different levels of exposure that aid in the language assessment of bilingual children. Nonword repetition accurately distinguished children with and without language impairment regardless of bilingual exposure.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.061
GPT teacher head0.421
Teacher spread0.360 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations81
Published2014
Admission routes2
Has abstractyes

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