Creativity, Labour, and the Politics of Profit in the Improvised Music Field
Bibliographic record
Abstract
Despite its ubiquity in everyday life and non-Eurocentric musics, improvisation is discursively constructed in Western culture as an edgy, radical, and subversive activity. Although there is no agreement on an ontology of improvisation in either the popular or scholarly domains, recent writings in music studies and the humanities propose that the improvisatory practices of jazz and related musics can be applied to contexts outside of the arts to address static, unethical, or otherwise outmoded ways of organizing society. A problematic example of this cross-fertilization of ideas is the recent trend in management studies of exploring the value in bringing improvisatory practices traditionally associated with the arts into the business field. Proponents of this idea suggest that applying the operational frame of improvisation to business organization can help companies develop new products and labour practices to respond to the shifting demands of the market. In this article I argue that much of the recent work in management studies fails to meaningfully address the real world material conditions under which artists work, nor the ethical implications of incorporating the practices of economically marginalized subjects into profit-based enterprises. Drawing on ethnographic fieldwork conducted in the improvised music fields in London, England, and Toronto, Canada, I will explore the issues raised by this trend in management studies by putting the ideas and experiences of improvisers in dialogue with those of theorists who are looking to the arts for new and innovative business strategies.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.145 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".