Music Theatre on Zoom: Significance in Integrating Virtual Performance in the Lives of Older Adults
Bibliographic record
Abstract
Music Theatre on Zoom: Significance in Integrating Virtual Performance in the Lives of Older Adults Our study aims to explore the affordances of conducting research with an online choir for older adults and to examine how participant-observer experiences can inform our practices as performing musicians, music educators, and researchers. Studies show that participating in music and dance classes facilitates creativity, community, belonging, and enjoyment (Creech et al., 2013). Similarly, music enhances self-identity and reminiscing (McCabe, Greasley-Adams, & Goodson, 2013), while improvisational dance can foster a sense of expanded time and space (Almqvist, 2020). However, not enough is known about music theatre in a virtual platform and the role of participant-observers within this setting. To fill this research gap, we used a Collaborative Scholarly Personal Narrative methodology (Nash & Viray, 2013; Nash, 2019) as a means of interrogating our shared experiences as researchers on Rise, Shine, Sing!, a weekly, Kingston-based virtual music theatre program of approximately 20–30 participants. Drawing on observation reports collected over five months, we reflected upon participants’ levels and patterns of engagement, the choice of repertoire, and possible areas for improvement. We also reflected upon the advantages and disadvantages of conducting sessions online as opposed to in-person. Through a process of individual and shared inquiry, our findings revealed that building a sense of community led to increased engagement, with participants becoming more comfortable sharing their talents and conversing with others. Furthermore, our experiences with this online community of researchers and participants allowed for new forms of engagement and programming which will likely benefit our professional practices in significant and exciting ways.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".