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Record W1771800576 · doi:10.21083/csieci.v1i1.9

Tripping On Wires: The Wireless Body: Who is Improvising?

2004· article· en· W1771800576 on OpenAlexvenueno aff
Pauline Oliveros

Bibliographic record

VenueCritical Studies in Improvisation / Études critiques en improvisation · 2004
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
FundersUniversity of California, San Diego
KeywordsMerge (version control)MusicalEmbodied cognitionLaptopImprovisationVisual artsAestheticsComputer scienceArt

Abstract

fetched live from OpenAlex

The author has been tripping on wires onstage and offstage for half a century of technological change in music. She has observed the trajectory of the disembodiment of music from analog instruments to tape to laptop computer performance. The process of disembodiment that began millennia ago with the use of instruments that distanced the musical use of voice, hands and feet with the body in resonance has accelerated in the 20th century to a crisis point. How do musicians, composers and audiences relate with the disconnected body and disembodied music? The call now seems to be to reintegrate the body with technology. The solution seems to indicate that the technology and body will merge in a re-embodied wireless future.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.020
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0090.043
Scholarly communication0.0200.017
Open science0.0020.006
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0070.002

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 head0.048
GPT teacher head0.361
Teacher spread0.313 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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