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Record W2160646336 · doi:10.1145/2617995.2617996

Choreography as Mediated through Compositional Tools for Movement

2014· article· en· W2160646336 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
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsChoreographyComputer scienceRepresentation (politics)Variety (cybernetics)Movement (music)Process (computing)Expression (computer science)Feature (linguistics)Experiential learningHuman–computer interactionReflection (computer programming)DanceArtificial intelligenceVisual artsProgramming languageAestheticsLinguisticsArtSociology

Abstract

fetched live from OpenAlex

Choreography is the art of crafting movement, developed through a long history of techniques. Like other compositional processes, choreography is a complex creative process that explores a variety of formal procedures that can result in unique artistic creations. Current computational systems for assisting choreography tend to be idiosyncratic, with emphasis on different feature sets of the compositional process (including movement, structure or expression). In this paper we examine existing technological systems for supporting choreography and group them by their purpose: reflection, generation, real-time interaction, and annotation. We then analyze these system features using Laban Movement Analysis, a comprehensive language for movement description, representation, expression and performance. Our paper articulates the relative benefits of these systems based on experiential aspects of choreography, and posits future directions of intelligent systems for supporting and partnering with choreography.

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 score0.881
Threshold uncertainty score0.716

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)0.0010.000

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 head0.015
GPT teacher head0.230
Teacher spread0.214 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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