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Record W4404413771 · doi:10.4000/12i35

L’ars subtilior de Lachenmann

2008· article· fr· W4404413771 on OpenAlexaff
Didier Guigue

Bibliographic record

VenueFiligrane · 2008
Typearticle
Languagefr
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Serynade, composée en 1997-98 et à ce jour le grand-œuvre d’Helmut Lachenmann (1935) pour le piano, est basée sur des schémas dialectiques qui mettent en jeu un double rapport, d’une part entre des éléments différenciés sur le plan acoustique, et d’autre part entre des matériaux qui entretiennent des relations complexes avec la tonalité et son « aura ». Cet article a pour but de proposer un modèle analytique qui puisse prendre en compte ces aspects, fondamentaux pour la compréhension de la musique de ce compositeur. Le modèle s’appuie, entre autres, sur la théorie compositionnelle que Lachenmann a lui-même développée depuis les années soixante, ainsi que sur un système typologique et structurel original élaboré à cet effet.

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)0.004
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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.007
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.004

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 head0.040
GPT teacher head0.225
Teacher spread0.184 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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