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Record W7128598133 · doi:10.7202/1122856ar

Repenser l’histoire : un petit arbre généalogique du spectralisme

2025· article· fr· W7128598133 on OpenAlexvenueno aff
Marilyn Nonken

Bibliographic record

VenueCircuit Musiques contemporaines · 2025
Typearticle
Languagefr
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsKaleidoscopeLafora diseasePeriod (music)

Abstract

fetched live from OpenAlex

Cet article examine le rôle jusqu’alors non reconnu joué par les expérimentateurs américains dans l’histoire de la musique spectrale, en considérant les relations entre Edgard Varèse, John Cage, Morton Feldman et James Tenney, et qu’ils entretenaient avec Jean-Claude Risset, François Bayle, Hugues Dufourt et Gérard Grisey. L’exploration historiographique, qui s’appuie sur une correspondance inédite, suggère que l’attitude spectrale est apparue simultanément des deux côtés de l’Atlantique dans les années 1970, à partir de sources et de géniteurs communs ; son émergence peut être attribuée en partie à l’étendue de l’influence de Varèse, qui a été bien démontrée, ainsi qu’à la portée des innovations de Tenney, qui ne l’a pas été. L’influence de Feldman, largement absente des études antérieures sur Tenney et la musique spectrale, est réexaminée. Malgré leurs points de vue communs sur la musique, c’est Feldman qui a ardemment défendu la reconnaissance d’une tradition musicale expérimentale désormais indissociable de l’Europe ou de l’Amérique. Si Tenney partageait en privé la position de Feldman, il a finalement adopté une position isolationniste, occultant cette histoire.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.016
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.212
Teacher spread0.196 · 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
Published2025
Admission routes1
Has abstractyes

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