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Record W2548960570

Big Gestures?: Factors that Influence Gesture Visibility

2014· article· en· W2548960570 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicUsability and User Interface Design
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGestureVisibilityAction (physics)Human–computer interactionComputer scienceFace (sociological concept)PsychologyCognitive psychologyCommunicationArtificial intelligenceLinguisticsGeography
DOInot available

Abstract

fetched live from OpenAlex

In many scenarios that involve digital systems, it is beneficial to maintain awareness of other people’s actions. During face-to-face communication with fellow humans, this is accomplished by ges-tures. The recent interest in gestural interfaces offers the possibil-ity to transfer this paradigm to human-computer interaction. Pre-viously, researchers exclusively discussed gesture size as a con-tributing factor for awareness maintenance through gestures. However, this might be only one piece of the picture, as other factors might be equally important. We studied small (tablet-sized), medium (monitor-sized), and large (full-arm) gestures. Our study showed that, although size does have significant effects, there are other factors that influence awareness maintenance. Our results provide empirical guidance about the ways that gesture size affects awareness, show that other factors, such as gesture morphology, influence awareness, and suggest that gestural inter-action has potential for improving group awareness in co-located environments.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.346
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.247
Teacher spread0.217 · 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

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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