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Record W2024046182 · doi:10.4018/jcicg.2010010106

Calligraphic Video

2010· article· en· W2024046182 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

VenueInternational Journal of Creative Interfaces and Computer Graphics · 2010
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsIntuitionHuman–computer interactionLeverage (statistics)Computer scienceGestureGraphical user interfaceComputer graphics (images)MultimediaArtificial intelligenceCognitive scienceProgramming language

Abstract

fetched live from OpenAlex

Since 1984, Graphical User Interfaces have typically relied on visual icons that mimic physical objects like the folder, button, and trash can, or canonical geometric elements like menus, and spreadsheet cells. GUI’s leverage our intuition about the physical environment. But the world can be thought of as being made of stuff as well as things. Making interfaces from this point of view requires a way to simulate the physics of stuff in realtime response to continuous gesture, driven by behavior logic that can be understood by the user and the designer. The author argues for leveraging the corporeal intuition that people learn from birth about heat flow, water, smoke, to develop interfaces at the density of matter that leverage in turn the state of the art in computational physics.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.297
Teacher spread0.285 · 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