Bibliographic record
Abstract
Group musical improvisation represents a profoundly collaborative creative process. The improvised framework demands that musicians collectively and spontaneously negotiate a set of dynamic musical and non-musical challenges. Similarly, in the post-fordist global marketplace, unexpected challenges have become a quotidian part of the business experience. Just as a group of musical improvisers must negotiate sudden musical changes, unanticipated changes in the marketplace demand a collaborative creative response. Since the early 2000s a wide variety of corporations have begun looking to group musical improvisation as a model for corporate design. Corporations ranging from Starbucks to Procter & Gamble to Research In Motion have hired improvising musicians to run seminars and workshops in order to develop more improvisatory – and more profitable – business practices. Complicating this narrative, however, is the ethic that is commonly attached to improvised musical practice: as numerous scholars have suggested, improvised musics frequently emerge from marginalized communities around the world, and often represent kinds of musicking that purposefully challenge the logics of the free market economy, especially in its present neoliberal guise. In this article, I suggest that this species of arts-based consultancy - together with a huge and ever-expanding body of writing on "creativity" - operates to provide an aesthetic aspect to "creative" and "knowledge-based" labour in the postindustrial North American "knowledge economy," and by extension, to neoliberalism itself. I argue that this aestheticization of neoliberal economics threatens to occlude the profoundly socially destructive impact of the laissez-faire neoliberal regulatory regime that has taken hold of economic and social policy on an increasingly global scale over the last three decades. Finally, however, I explore how the lessons of improvisation studies might intervene in the corporate appropriation of 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.052 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".