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Record W2010007259 · doi:10.1145/2556288.2557219

Making big gestures

2014· article· en· W2010007259 on OpenAlex
Adrian Reetz, Carl Gutwin

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
TopicHand Gesture Recognition Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGestureObservabilityComputer scienceIdentifiabilityHuman–computer interactionArtificial intelligenceMachine learningMathematics

Abstract

fetched live from OpenAlex

Co-located work environments allow people to maintain awareness by observing others' actions (called consequen-tial communication), but the computerization of many tasks has dramatically reduced the observability of work actions. The recent interest in gestural interaction techniques offers the possibility of recreating some of the noticeability of previous work actions, but little is known about the observability and identifiability of command gestures. To investigate these basic issues, we carried out a study that asked people to observe and identify different sizes and morphologies of gestures from different locations, while carrying out an attention-demanding primary task. We studied small (tablet sized), medium (monitor-sized), and large (full-arm) gestures. Our study showed that although size did have significant effects, as expected, even small gestures were highly noticeable (rates above 75%) and identifiable (rates above 69%). Our results provide empirical guidance about the ways that gesture size, morphology, and location affect observation, and show that gestural interaction 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.625

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 science0.0000.000
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 head0.051
GPT teacher head0.278
Teacher spread0.226 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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