Bibliographic record
Abstract
This paper was written to help address the tenuous status of research-creation at the University of Toronto, where I am a Doctor of Musical Arts candidate. There, I devised a “feedback saxophone” system in which I combine the tenor saxophone with various microphones and speakers to encourage and control acoustic feedback. The DMA program at U of T is classified as professional, so the premise of centering my thesis around my feedback saxophone practice was met with some healthy skepticism. This was not because it was viewed as uninteresting, but because creative practice is not typically considered a justifiable form of research in thesis writing. To therefore bolster research-creation as a legitimate form of scholarly inquiry and to build a model for my own research in music, I aim to answer two questions, insofar as they pertain to my research-creation project: (1) “How is creative practice research?” and (2) “What methods are appropriate for carrying out my creative practice as research?” In answering the first, I draw from the literature to demonstrate how research-creation is a form of knowledge gener- ation that complements conventional modes of investigation. Following this, I examine different categories of research-creation and illustrate them on a music research “compass” to facilitate comparison and understanding. To answer the second question, I discuss two relevant research-creation methodologies and combine them to construct my own “problem-practice-exegesis” approach. I conclude by detailing how I carry out my research using this methodology. Through this work, I endeavor to provide a practical model for graduate artist-researchers who are interested in integrating their creative practices with thesis writing and to contribute to the validation of research-creation within Canadian graduate music programs and beyond.
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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 | 0.438 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.017 | 0.102 |
| Scholarly communication | 0.037 | 0.036 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".