L’échantillonnage dans l’improvisation : Rencontre de deux instigateurs du free jazz avec un jeune artiste de la scène noise à New York
Bibliographic record
Abstract
In this paper we report on an experimental study that brought two free jazz instigators, the drummer Todd Capp and multi-instrumentalist Daniel Carter, to musically meet Mikey Holmes, a young noise artist in New York in May 2014. Throughout an analysis of the improvisation process of these three musicians, our study addresses both the social and artistic continuity between different improvisation genres and generations. The two improvisation meetings have been filmed and recorded to allow musicians’ self-evaluation. We include two videos in the article to share the musical result of these meetings with the readers. We use musicians’ quotes to shed light on issues ranging from performance and recording time, the use of contextual sounds, transmusicality, free improvisation and the links between music and politics in New York between the late fifties’ jazz giants and today’s improvised music. Thanks to the issues tackled, we show how the subversiveness of a particular music and its resistance to time are related, and we suggest there is a link between the sustainability of musical recordings and their conditions of studio production. Our interdisciplinary approach allows us to confront the art of sampling with improvised performance and to question the social and political aspects included in an improvisation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".