Digital musicianship in post-pandemic popular music education
Bibliographic record
Abstract
The COVID-19 pandemic has brought about dramatic changes in popular music education, underscoring the importance of technology in both practice and transmission. Nevertheless, the celebration of technological integration as a one-stop solution has led to some challenges remaining overlooked and unsolved, resulting in an increasing mismatch between popular music education and the growth of online musical engagement among young people. Framed by the notion of digital musicianship, this position paper presents an overview of the adoption of technology in popular music education within the lockdown period, in the process raising concerns about whether such revisions can adequately address the challenges faced by popular music education. This leads to a discussion of a potential revamp of digital musicianship as a response to the continued and expanding presence of technology within popular music education and the post-pandemic teaching and learning environment. The authors assert that digital musicianship should encompass learners’ ability to perceive and adopt technologies for online and remote music-making, and critically evaluate their validity and quality in various contexts. Additionally, there is a need to critically address the homogeneity of musical cultures and technological determinism embedded within the design of music technology, alongside tackling the issues of inequality and accessibility.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".