Thinking outside the Zoom box: Discovering resilience, innovation and creativity for large instrumental ensembles during the pandemic
Bibliographic record
Abstract
This paper provides readers with insights and strategies to tackle challenges of various remote and in-person large ensemble rehearsal situations, as well as hopefully inspires others to find the opportunities through the obstacles. The authors provide tips and strategies for creating innovative and cross-disciplinary projects and providing valuable experience for the ensemble students in virtual, hybrid and socially distanced in-person educational settings. Strategies presented are gathered from the authors’ first-hand experiences with their large orchestral ensembles (ranging from 50–70 students) during the pandemic. Finally, the authors provide insights on what has worked well, challenges faced, technologies applied and lessons learned during the process. This paper also discusses various creative strategies to highlight collaboration and create a sense of community and belonging in a remote environment. Readers will gain ideas regarding unique teaching concepts for the music ensemble in the current environment including fully remote instruction, hybrid instruction and in-person settings. Matters such as utilising the audio Jamulus platform, engaging students in synchronous format, wellness for the instructor and students, finding value and motivation and embracing technology will be explored throughout the paper.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".