Facilitating Later-Life Community Music Learning in Offline and Online Contexts
Bibliographic record
Abstract
Abstract While extensive research has demonstrated the social, emotional, and cognitive benefits that may be associated with participatory music-making among older adults, there has been less attention paid to the actual tools (e.g., musical instruments, resources) that can provide a ‘way in’ to creative musical development in later life, and limited research has focused on the intersection between the pedagogies, tools, and contexts, and the ways in which those factors may influence creative or developmental outcomes. This chapter focuses on two community groups of novice adult music learners, where the aim was to identify the musical tools and the pedagogies that would support creative expression and musical development. The two groups used a range of instruments, including rock band instruments, percussion, and iPads, in activities that focused on learning musical concepts, creative improvisation and songwriting, and learning to perform well-loved songs. Both groups pivoted from in-person to online music-making during the global COVID-19 pandemic, when the iPads became a central tool. Data were collected through video recordings of the weekly offline music sessions, facilitator field notes, and through focus groups and interviews with group participants. The chapter will explore the following questions: What are the facilitation approaches that can support creative music-making with older adult novice musicians, within offline (in-person) and online environments? What are the learner perceptions associated with the use of different instruments, including mobile technologies (iPads), in the context of later-life creative music participation?
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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 | 0.002 | 0.004 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".