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Record W2398504104

Spatial gestures point the way: A broader understanding of the gestural referent

2013· article· en· W2398504104 on OpenAlexaff
Kinnari Atit, Ilyse Resnick, Thomas F. Shipley, Carol J. Ormand, Cathryn A. Manduca, Tilbe Göksun, Basil Tikoff

Bibliographic record

VenueeScholarship (California Digital Library) · 2013
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsCarleton University
FundersNational Science Foundation
KeywordsReferentGesturePoint (geometry)CommunicationComputer scienceLinguisticsCognitive sciencePsychologyArtificial intelligenceMathematicsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

We investigated the use of iconic and deictic gestures during the communication of spatial information.Expert structural geologists were asked to explain one portion of a geologic map.Spatial gestures used in each expert's response were coded as deictic (indicating an object in the conversational space), iconic (depicting an aspect of an object or event), or both deictic and iconic (indicating an object in the conversational space by depicting an aspect of that object).Speech paired with each gesture was coded for whether or not it referred to complex spatial properties (e.g.shape and orientation of an object).Results indicated that when communicating spatial information, people occasionally use gestures that are both deictic and iconic, and that these gestures tend to occur when complex spatial information is not provided in speech.These results suggest that existing classifications of gesture are not exclusive, especially for spatial discourse.

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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.007
Scholarly communication0.0040.007
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.255
Teacher spread0.211 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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Same venueeScholarship (California Digital Library)Same topicHearing Impairment and CommunicationFrench-language works237,207