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Record W3203598861 · doi:10.3390/sym13101776

Hand Preference in Adults’ Referential Gestures during Storytelling: Testing for Effects of Bilingualism, Language Ability, Sex and Age

2021· article· en· W3203598861 on OpenAlexafffund
Elena Nicoladis, Safi Shirazi

Bibliographic record

VenueSymmetry · 2021
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsUniversity of AlbertaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGestureNeuroscience of multilingualismPsychologyPreferenceLateralization of brain functionLinguisticsTest (biology)Cognitive psychologyMathematics

Abstract

fetched live from OpenAlex

Previous studies have shown that gestures are mediated by the left hemisphere. The primary purpose of this study was to test whether most gestures are also asymmetrical, i.e., produced with the right hand. We also tested four predictors of the degree of right-hand gesture use: bilingualism, language ability, sex, and age. These factors have been related to differences in the degree of language lateralization. English monolinguals, French–English bilinguals, and French monolinguals watched a cartoon and told the story back. For the gestures they produced while speaking, we calculated the percentage produced with the right hand. As predicted, the majority of gestures were right-handed (60%). Bilingualism, language ability, and age were not significantly related to hand choice in either English or French. In English, males tended to produce more right-handed gestures than females. These results raise doubts as to whether hand preference in gestures reflects speech lateralization. We discuss possible alternative explanations for a right-hand preference.

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 score0.002
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.283
Teacher spread0.250 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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