How Can I Keep From Singing? A Phenomenological Study of the Experiences of Ontarian Choral Music Educators During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic disrupted the practices of choral music educators in Ontario for three school years. Previous research has focused on particular aspects of teaching music during the pandemic including the use of technology in music education, the experiences of educators during the initial lockdown, hybrid learning in post-secondary settings, and the impact of COVID-19 on choral programs in Canada a year into the pandemic. What has not yet been studied is how the pandemic impacted choral music educators. To understand these impacts, we must first understand the phenomenon of choral music teaching during the pandemic. This phenomenological study explores the lived experiences of secondary choral music educators teaching in the Greater Toronto Area during and after the COVID-19 pandemic. Through the exploration of the professional realities of secondary choral music educators between March 2020 and June 2023, this research produces in-depth descriptions of the phenomenon while also uncovering possible impacts of the pandemic on choral programs. Through careful analysis, three themes granted further insight into how participants experienced the phenomenon; the ways in which the participants described adapting to change, experiencing loss, and navigating the disconnect between their expectations and their realities. The findings from this study contribute to the growing research about the impacts of the pandemic on music education and will increase understanding of post-pandemic realities in Ontarian secondary choral music programs. Keywords: choral music education, phenomenon, pandemic, COVID-19, emergency online learning, hybrid learning, recovery.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".