Musical engagement at any cost? Community music leaders’ embrace of technology-enabled music-making during the COVID-19 pandemic
Bibliographic record
Abstract
In an alternative universe to popular music (PM) education, many community music (CM) educators turned to technology during unprecedented pandemic disruptions, attempting to maintain group music-making and social connections. This study investigates CM technology-aided pandemusicking, drawing from case studies of twelve Canadian leaders, and finding that music fields, values and goals were blurred. These leaders often used recorded and live internet music-making, which required adopting digital technologies that align more closely with PM fields than their traditional practices. Pandemusicking was often a difficult solution, but leaders were aided by increased consumption and skill-partnerships. Nuanced considerations from literature on media culture counter utopian rhetoric about tech-enabled democracy, consumption and participation while prompting reflections on broader implications of a technological world that leaves some music participants and educators behind. This outcome has vital implications for leaders with inclusive goals, who work with wide age ranges, and it suggests potential roles of PM education and educators.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".