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Record W2135142995 · doi:10.3109/07434618.2014.921240

Monolingual and Bilingual Children With and Without Primary Language Impairment: Core Vocabulary Comparison

2014· article· en· W2135142995 on OpenAlexaff
Manon Robillard, Chantal Mayer-Crittenden, Michèle Minor-Corriveau, Roxanne Bélanger

Bibliographic record

VenueAugmentative and Alternative Communication · 2014
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsLaurentian University
Fundersnot available
KeywordsVocabularyCore (optical fiber)PsychologyLinguisticsNeuroscience of multilingualismLanguage impairmentAugmentative and alternative communicationDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

Core vocabulary is an important component of augmentative and alternative communication (AAC) systems for school-aged children who have complex communication needs. One method of identifying core vocabulary for these individuals is to study the vocabulary of speaking children. To date, the use of core vocabulary by speaking bilingual children has not been well documented. The present study compared the core vocabulary used by children who are monolingual (French), and bilingual (French-English; English-French). We also gathered and compared language samples from French-speaking children identified as having primary language impairment (PLI), with the goal of better understanding the language differences demonstrated by children with this disability. Language samples were collected from a total of 57 children within a school setting, in a region where French is a minority language. Contrary to the hypothesis, the analysis of language transcripts revealed that there were no important differences between the core words from the groups studied.

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)0.002
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.340
Teacher spread0.317 · 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

Citations36
Published2014
Admission routes1
Has abstractyes

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