Final Report - 2023. A Community Music Approach to Collaborative Sonic Spaces in WebXR
Bibliographic record
Abstract
The purpose of this project is to introduce extended reality (XR) practices for music group improvisation to a selected group of students from Community Music Schools of Toronto (CMST), and to collaborate on a multiplayer virtual reality (VR) prototype with the students as co-creators. Our project allows for participatory, collaborative, and co-creative interactions that are vital to developing skills in music creation and learning music concepts. Our main intention is to increase digital literacy and accessibility of emerging media and music for youth aged 10-17 years old in Canada, and add virtual reality to the music curriculum at the Community Music Schools of Toronto.\n\nMore concretely, our participatory research-creation project was conducted with The Senior Jam Class at CMST in the spring term 2023. The class was composed of five students from 13 to 15 years-olds, whose main focus was to learn to jam together. The class itself was headed by Allison Cameron, who is a professional composer, performer and improvising musician in Toronto. She has a long standing career in improvising and performing on electronic keyboards, ukulele, banjo, piano, mini amplifiers, radios, crackle boxes, cassette tapes, miscellaneous objects and toys. It was crucial for us that we integrate the VR project as much as possible within these improvising practices and the class curriculum in discussions with Ms Cameron, who also participated in our workshops together with her teaching assistant Jevoy Jennings. Thanks to\ntheir welcoming approach, we were able to create a safe and collaborative space while also being able to share knowledge and different music practices among ourselves.\n\nWe considered inclusivity and accessibility through the practice of co-creation with the students (including them in all of the project phases - from ideation to prototyping). We provided them with an environment for creating music that differs from traditional musical instrument performance and leverages their skills in listening, improvisation, empathy, and imagination, all core principles of collaborative group music making.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.019 |
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".