Embracing the Unexpected: Improvisational Pedagogy for Collaborative Music-Making in a Care Home Setting
Bibliographic record
Abstract
Abstract This chapter explores the idea of pedagogy as an iterative, evolving improvisation. We discuss two creative project case studies in the context of long-term care where residents were given the opportunity to explore collaborative improvisation and storytelling through music, using the Soundbeam, a motion-detecting musical instrument, along with iPads and traditional percussion. Although there is a growing body of literature on the benefits of creative music-making during later life (Creech et al., 2013; Creech et al., 2014; see Chapter 5 of this volume), particularly with regard to the social and cognitive benefits, there is significantly less research that examines the pedagogical approaches that can support creative and engaged music-making among residents in long-term care, especially from the perspective of the practitioner. Accordingly, this chapter discusses pedagogy as improvisational performance (Costa & Creech, 2019; Sawyer, 2004; Taylor & Francis, 2019), exploring the implications of a flexible and reflective facilitator paradigm that reconciles planning and structure with the need for in-the-moment flexibility informed by reflection-in-action. In this same spirit, and since one of the authors is a music therapist, we present our findings in dialogue format as a more direct window into this perspective, which is unique among the chapters in this collection. We address the following questions: What are the principles and practices of an improvisational pedagogy for creative music-making with long-term care residents? Is it possible to plan for the unexpected in later-life music-making workshops?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".