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Record W2946452691 · doi:10.30535/mto.25.1.1

Improvisatory Exercises as Analytical Tool

2019· article· en· W2946452691 on OpenAlexaff
Valentina Bertolani

Bibliographic record

VenueMusic Theory Online · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTimbreOrchestrationMusicalContext (archaeology)ImmediacySet (abstract data type)AestheticsRepetition (rhetorical device)Extant taxonPeriod (music)Action (physics)Visual artsPsychologyComputer scienceArtEpistemologyHistoryLinguisticsPhilosophyBiology

Abstract

fetched live from OpenAlex

Avant-garde improvised music tends to elude analytical attempts, which have so far mostly focused on transcribing and describing sound parameters (e.g., pitch, timbre, texture, etc.) in the extant recordings. However, this does not account for the irreducible immediacy of the improvisatory practice, whose recordings are just the tip of the iceberg of a more multifold and varied production (often not recorded). This issue, inherent to the type of creative process at hand, suggests that, when analyzing, we should also take into consideration what aesthetical principles influenced the choice of a succession of musical events over another. The principles of action-reaction and of non-repetition of the musical material were the guiding elements of the improvisatory practice of the Italian Gruppo di Improvvisazione Nuova Consonanza (GINC) in the period 1965–1969. To adhere to these principles, GINC created a set of exercises to be practiced by members during the rehearsals. In this article I will analyze three examples from a 1967 video recording of the Gruppo di Improvvisazione Nuova Consonanza using these exercises to understand and comment the choices made on the spot by the improvisers. This strategy affords a better awareness of the group actions within the improvisatory context.

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)0.004
metaresearch head score (Gemma)0.009
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: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0020.006
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.023
GPT teacher head0.237
Teacher spread0.214 · 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
GenreMethods

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

Citations5
Published2019
Admission routes1
Has abstractyes

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