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Record W2528568763 · doi:10.21083/csieci.v12i1.3726

Improvisation in Process

2018· article· en· W2528568763 on OpenAlexvenueno aff
Bennett Hogg

Bibliographic record

VenueCritical Studies in Improvisation / Études critiques en improvisation · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsImprovisationParallelsMusicalProcess (computing)Focus (optics)EpistemologyAestheticsSociologyPsychologyCognitive sciencePhilosophyArtLiteratureComputer scienceVisual arts

Abstract

fetched live from OpenAlex

The language we use to talk about things, as is self evident, affects what we are able to think and do. Western musical culture, as Small has eloquently argued, has a strong focus on the musical work, music as a thing rather than an activity. There are parallels with judicial process being focused on judgments, the outcomes of the process. However, bringing judicial process and musical improvisation into a dialogue resonates the extent to which terminologies may block understanding and possibilities for progress/alternative views. In this paper I argue that the concept of the musical work is inimical to an understanding of improvisation, and speculate on the extent to which this is paralleled by the notion of judgment as outcome rather than process.

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)0.014
metaresearch head score (Gemma)0.022
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: Other · Consensus signal: Other
Teacher disagreement score0.018
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.098
Scholarly communication0.0180.016
Open science0.0020.010
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.079
GPT teacher head0.474
Teacher spread0.395 · 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
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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