Bibliographic record
Abstract
The experience of teaching in K-12 school music programmes across Canada changed dramatically for music educators in March 2020 because of the emergency created by the COVID-19 pandemic. Results from Singing in Canadian Schools: COVID-19 Impact Survey (Morin & Mahmud, 2021) confirmed that music and choral programmes suffered significantly, but that positive outcomes also arose from the innovative music teaching approaches that evolved during this unsettling time. A full discussion of categorised data themes resulting from an analysis of the written comments of Canadian music teachers (N = 375) illuminates these positive outcomes – for music education and singing, and music educators and their students. Music teachers explored broader music content in classes and offered students more diverse musical experiences. They skillfully incorporated more technology into music programmes, emphasised individual musical development over that of ensembles, and built stronger personal connections with students. Moreover, music teachers benefitted from accessing an array of online professional learning opportunities. The theory of action learning (Brockbank & McGill, 2003; Revans, 1982, 2008) is posited as a possible explanation for music educators’ adaptive and constructive responses to the pandemic crisis. The experiences of music teachers during the COVID-19 crisis provides the field with an opportunity to re-think school music education and rebuild it for the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".