Pandemic Profits: The Hidden Privatization of US and Canadian Music Education
Bibliographic record
Abstract
Employing Stephen Ball’s notion of network governance, this study examines the relationships between private companies, non-profit organizations, and public institutions involved with music education in the United States (US) and Canada during the COVID-19 pandemic. To identify which public and private actors had a hand in shaping music education policy, we traced the digital music resources three major professional organizations recommended at the height of the pandemic. Also, we examined these organizations’ equity discourses surrounding the adoption of digital music-making services in public schools. Informed by the work of critical theorists Ball, Deborah Youdell, Naomi Klein, and Mark Fisher, our analysis suggests that the uptake of digital technologies during the pandemic may have deepened the hidden privatization of public schooling in the US and Canada. We conclude by advocating for a techno-skeptical approach to music technology and a re-investment in schooling as a public good.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".