The IAFOR International Conference on Arts and Humanities – Hawaii 2022 Official Conference Proceedings
Bibliographic record
Abstract
This article 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 utilizing 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 article.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.291 | 0.091 |
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".