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Record W1987466178 · doi:10.1162/comj_a_00147

Following Gesture Following: Grounding the Documentation of a Multi-Agent Music Creation Process

2012· article· en· W1987466178 on OpenAlexaff
Guillaume Boutard, Catherine Guastavino

Bibliographic record

VenueComputer Music Journal · 2012
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsDocumentationComputer scienceGestureProcess (computing)Human–computer interactionMusicalAbstractionGrounded theoryData scienceArtificial intelligenceQualitative researchSociologyVisual artsArtEpistemologyProgramming language

Abstract

fetched live from OpenAlex

The documentation of electroacoustic and mixed musical works typically relies on a posteriori data collection. In this article, we argue that the preservation of musical works having technological components should be grounded in a thorough documentation of the creative process that accounts for both human and nonhuman agents of creation. The present research aims at providing a ground for documentation policies that account for the creative process and provide relevant information for performance, migration, and analysis. To do so, we analyzed secondary ethnographic data from a two-year creation and production process of a musical work having a focus on gesture following. Using grounded theory, we developed a conceptual framework with different levels of abstraction and consequent levels of transferability to other creative contexts. Finally, we propose several paths for grounding a subsequent documentation framework in this conceptual framework.

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)0.016
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0050.013
Scholarly communication0.0080.012
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.299
Teacher spread0.260 · 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 designQualitative
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

Citations8
Published2012
Admission routes1
Has abstractyes

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