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Record W4242413028 · doi:10.22215/etd/2015-11112

Intent-Gesture Relationships for Collaborative Information Visualization

2015· dissertation· en· W4242413028 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
Typedissertation
Languageen
FieldComputer Science
TopicUsability and User Interface Design
Canadian institutionsCarleton University
Fundersnot available
KeywordsGestureVisualizationComputer scienceGraphHuman–computer interactionPsychologyArtificial intelligenceTheoretical computer science

Abstract

fetched live from OpenAlex

In this study we look at the relationship between gestures and intents when pairs of participants are collaborating around a large display with a graph. We aimed to find out what gestures paired with which intents, which gestures participants would find suitable for various intents, and how our findings could influence designing interactions with graphs being used for collaborative analysis work. We studied 8 pairs of participants and found 10 frequent gestures and 11 frequent intents. An exploration of the relationship between these gestures and intents found 15 frequent co-occurrences. We analyzed these findings and then proceeded to make design suggestions for enabling co-located collaboration interaction using large multi-touch displays. Throughout, we used a theory of technical intersubjectivity to guide our research. In particular, this helped us to position large multi-touch displays as enablers of intersubjective interactions, which facilitated our design process.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.711
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.062
GPT teacher head0.325
Teacher spread0.262 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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