Reframing, embodying and in-betweening: A conversation about experiences of doing practice-based research and research-creation
Bibliographic record
Abstract
This article presents a conversation between one professor and two students in the Ph.D. programme at The Creative School, Toronto Metropolitan University, the first in Canada to offer a dedicated practice-based Ph.D. opportunity to candidates from all creative disciplines. The discussion covers our own experiences of practice-based research and research-creation and describes how this can be an extraordinarily powerful way to explore issues of identity, community, exclusion and inclusion, creativity and human existence. We each share our individual pathways to practice-based research through the lens of garment-making, performance and music making, and how we have each found a place in the university to explore ideas and research through these mediums. We consider our experiences of making, doing and thinking about practice, and what this means as an impactful and transformational source of knowledge.
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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 | 0.055 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.039 | 0.090 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".