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Record W3015346724 · doi:10.7202/1068383ar

La migration numérique d’une oeuvre pionnière avec live electronics. Mesa (1966) de Gordon Mumma

2020· article· fr· W3015346724 on OpenAlexvenueaboutno aff
Jonathan Goldman, Francis Lecavalier, Ofer Pelz

Bibliographic record

VenueRevue musicale OICRM · 2020
Typearticle
Languagefr
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Cet article résume les retombées d’un projet de reconstruction d’une oeuvre pionnière de live electronics, à savoir Mesa (1966) du compositeur étatsunien Gordon Mumma (né en 1935). Une reconstruction de cette oeuvre a été présentée dans le cadre du festival Montréal/Nouvelles Musiques le 27 février 2017 par l’auteur et les collaborateurs de cet article : Jonathan Goldman au bandonéon, Francis Lecavalier à la conception informatique, et Ofer Pelz à l’interprétation de la partie électronique. Au cours de cette « migration », l’oeuvre, fondamentalement analogique, a été reconçue pour un équipement numérique contemporain avec le consentement du compositeur. S’inscrivant nettement dans la lignée de l’École de New York cageienne, Mesa revêt un statut ontologique curieux : elle est le produit d’une indétermination passablement radicale. Comme c’est le cas pour l’oeuvre de Cage, Mesa et sa reconstruction numérique nous force à repenser l’ontologie et l’épistémologie de l’oeuvre musicale.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.017
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.010
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.022
GPT teacher head0.231
Teacher spread0.210 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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