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Record W2104204062 · doi:10.1109/tabletop.2007.20

Applying an Aesthetic Framework of Touch for Table-Top Interactions

2007· article· en· W2104204062 on OpenAlexaff
Thecla Schiphorst, Nima Motamedi, Norm Jaffe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMateriality (auditing)Computer scienceHuman–computer interactionGestureContext (archaeology)ModalSemantics (computer science)Artifact (error)AestheticsMeaning (existential)Artificial intelligenceArtPsychology

Abstract

fetched live from OpenAlex

In this paper, we propose a conceptual framework for understanding the aesthetic qualities of multi-touch and tactile interfaces for table-top interaction. While aesthetics has traditionally been defined as the visual appearance of an artifact, we promote a tactile aesthetics that is firmly rooted in the experience of use and interaction. Our model of tactile aesthetics comprises four distinct yet overlapping areas: 1.) Embodiment which grounds our framework within the larger philosophical context of experience. 2.) Materiality which emphasizes the importance of the physical shape, form and texture of interactive systems. 3.) Sensorial Mapping which is the creation of appropriate cross-modal relationships between touch and our other senses. 4.) Semantics of Caress which is the investigation into the meaning of touch which can then inform computational models of gesture recognition. We apply this framework to evaluate a series of tactile and multi-touch artworks and discuss how our model can benefit the design of future multi- touch systems.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.006
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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 designNot applicable
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

Citations12
Published2007
Admission routes1
Has abstractyes

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