Musical Totem: A Collaborative Composition Methodology During the Covid-19 Pandemic
Bibliographic record
Abstract
As part of the Action Research Network of the Americas, the Musical Learning Community is a collaborative group, founded during the COVID-19 global pandemic, that has brought together musicians, artists, and educators to generate shared experiences. As members of this community, we explore new ways for collaborative music-making. Through creative, cultural, and conceptual influences, the idea of the Musical Totem emerged as a collaborative music composition methodology to transcend geographical distancing. We sought interpretative freedom by adopting methods of the surrealist technique Cadavre Exquis (Exquisite Corpse) while relying on the rich concept of totems to find thematic material and set compositional parameters. The process was carried out using arts-based and autoethnographic research approaches, which provided insights into our creative musical responses and remote collaborative working processes. This endeavor showed us that symbolism can provide compositional and performative challenges and that, as a methodology, the Musical Totem can create freedom and constraints depending on the musician, the conceptual influences, and the instrumentation. We also learned that engaging in a collaborative music-making process led to increased community bonding through shared creative expression.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".