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Record W2148769184 · doi:10.1162/comj_a_00217

Live Coding in a Scalable, Participatory Laptop Orchestra

2014· article· en· W2148769184 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueComputer Music Journal · 2014
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLaptopComputer scienceScalabilityCitizen journalismCoding (social sciences)CyberneticsMultimediaWorld Wide WebSociologyArtificial intelligenceOperating systemSocial science

Abstract

fetched live from OpenAlex

Live coding (Collins et al. 2003 and other articles in this special issue of Computer Music Journal) is the central performance practice of the Cybernetic Orchestra, a laptop orchestra at McMaster University in Hamilton, Ontario, Canada. Inspired by the idea of participatory culture, the ensemble has been made open to a diverse and ever changing roster of participants, and may be likened to a human laboratory exploring this question: How is live coding scalable onto larger groups of people coming from diverse backgrounds? This article presents the practices that have developed during the first three years of the Cybernetic Orchestra's existence, starting with a summary of our human organization and physical infrastructure. The EspGrid software, developed for enhanced network synchronization and sharing, is reviewed before a final section presents the live coding practices that have crystallized around this specific collective of people, equipment, and code.

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)0.001
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 score0.659
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.050
GPT teacher head0.256
Teacher spread0.206 · 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