Popular Music as a Resource for Exploring Later-Life Identities in Music
Bibliographic record
Abstract
Abstract In this chapter, we address questions concerning the role of popular music in supporting creative ageing, and specifically in forming later-life musical identities. We focus on the experience of music-making in three 8-week cycles of weekly “rock hub” workshops, where older adult novice musicians learnt well-known popular songs, engaged in improvisation, and composed their own songs. This chapter draws on a thematic analysis of data that were collected via 42 structured interviews. Results have revealed older adults’ openness to engaging in a rock hub (comprising adapted rock band instruments) as a vehicle for creative expression through collaborative, exploratory musical activities. Furthermore, moments of significant interpersonal connection through popular music were recounted. Notwithstanding persistent challenges associated with a discourse of “I am not musical”, participants demonstrated the capacity for creative expression, musical development, and playful exploration of their identities in music. We offer another facet of what Bennett (2013) describes as a “reflexive understanding and use of popular music as a cultural resource in everyday life” for older people. While music, in general, is associated with improved wellbeing among older adults (Creech et al., 2014a), popular music, in particular, offered special opportunities for connection and creativity, pointing to a larger role it may play as well as to the need for further study in this area.
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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 | 0.001 | 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 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".