Promoting a musical lifecourse towards sustainable ageing: A call for policy congruence
Bibliographic record
Abstract
Today, individually perceived quality of life for a growing ageing population could be said to be significantly dependent on meaningful life experiences, social connectedness and a sense of purpose. In this article, we argue for a wider theorization of policy and the politics of ageing. The central aim is to reflect on understandings of ageing within music education and musical participation, and, in particular, shift the focus from active ageing – and the ways it might support the narrow agenda of music for older adults – to the potentials of holistic and sustainable learning and participation in music. To do so, we draw from the concept of policy congruence, presenting a vision of policy as a critical catalyst that may amplify parameters for concerted initiatives among multiple constituencies within music education. We argue these amplified parameters may afford renewed efforts towards transdisciplinary action that can support the actions of community musicians and strengthen their role as networked actors labouring in consonance with others in the growingly significant areas of lifelong learning and ageing populations. Our stance is that, if we can assume that music education and musical participation have a serious contribution to make in the lives and well being of individuals across the lifespan, including older adults, then we ought to consider how systematic policy engagement may actively contribute to appropriate allocation of resources and renewed pedagogical and organizational framings, which more directly use lifelong learning to support sustainable ageing.
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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 | 0.120 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.032 | 0.036 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.029 | 0.028 |
| 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".