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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".