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Record W2913487817 · doi:10.1017/s014271641800070x

How does language proficiency affect children’s iconic gesture use?

2019· article· en· W2913487817 on OpenAlexaff
Meghan Zvaigzne, Yuriko Oshima‐Takane, Makiko Hirakawa

Bibliographic record

VenueApplied Psycholinguistics · 2019
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of VictoriaMcGill University
Fundersnot available
KeywordsPsychologyGestureAffect (linguistics)Neuroscience of multilingualismLanguage proficiencyCognitionTask (project management)LinguisticsDevelopmental psychologyCognitive psychologyCommunicationMathematics education

Abstract

fetched live from OpenAlex

ABSTRACT Previous research investigating the relationship between language proficiency and iconic gesture use has produced inconsistent findings. This study investigated whether a linear relationship was assumed although it is a quadratic relationship. Iconic co-speech gesture use by 4- to 6-year-old French–Japanese bilinguals with two levels of French proficiency (intermediate and low) but similar levels of Japanese proficiency was compared with that of high-proficiency French monolinguals (Study 1) and Japanese monolinguals with similar proficiency to the bilinguals (Study 2). To control the information participants communicated, a dynamic referential communication task was used; a difference between two cartoons had to be communicated to an experimenter. Study 1 showed a significant quadratic relationship between proficiency and iconic gesture use in French; the intermediate-proficiency bilinguals gestured least among the three proficiency groups. The monolingual and bilingual groups with similar Japanese proficiency in Study 2 gestured at similar rates. It is suggested that children gestured for different reasons depending on their language proficiency and the cognitive resources available for the task.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.304
Teacher spread0.288 · 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.

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

Citations9
Published2019
Admission routes1
Has abstractyes

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