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Record W3039063313 · doi:10.1145/3401956.3404237

Performing Gesture and Time via an Emergent Database

2020· article· en· W3039063313 on OpenAlexaff
Doug Van Nort, Ian Jarvis, Kieran Maraj

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsYork University
Fundersnot available
KeywordsGestureComputer scienceStructuringImprovisationTemporalityEmbodied cognitionPerspective (graphical)Human–computer interactionGesture recognitionPerforming artsArtificial intelligenceMultimediaVisual arts

Abstract

fetched live from OpenAlex

This paper presents distinct modes of engaging with and structuring time, temporality and gesture as explicit units of semantic information. The work presents the newest iteration of a long-standing approach to instrumental system design and performance in which input gesture, sonic gesture and intermediate layers of information are stored as richly-structured "temporal semantic units" in a per-formable database, which can be queried via embodied action in order to be used in improvised performance contexts. This includes the mapping of temporal envelopes and the on-line training of machine learning algorithms. The history and lineage of ideas leading up to this current work are presented, as well as the novel system architecture and distinct modes of interaction that are composed from the perspective of the composer/performer engaged in the practice of electroacoustic improvisation.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.195

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 head0.020
GPT teacher head0.230
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2020
Admission routes1
Has abstractyes

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