Reconceptualizing “Music Making:” Music Technology and Freedom in the Age of Neoliberalism
Bibliographic record
Abstract
Recent initiatives by for-profit corporations and funding measures instituted by governments intend to support the preparation of students for careers in computer science and technology. Although such initiatives and measures can indeed increase opportunities for students' engagement with computer science and technology in K-12 schools, we question whose needs are being served, for what purposes, and at what cost. In particular, we ask whether music educators might be complicit in advancing technology that subordinates human needs-specifically students' interests in making music in their own creative ways-to modes of production that benefit certain dominant commercial interests in society. After discussing how current computer technology narrows students' choices, we counter this determinism by highlighting a music subculture that creates and appropriates music technologies for music-related purposes. Our example of the "chipscene" illustrates how music educators might reconceptualize "music making" through modification of existing music technology and thereby restore students' freedom to "reclaim making" in the age of neoliberalism.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.149 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".