Choreographing collaboration: A multilayered approach to somatic and site-oriented art practices
Bibliographic record
Abstract
Choreographing collaboration: A multilayered approach to somatic and site-oriented art practices is a research-creation thesis project that focuses on multiple sites of collaboration between bodies and spaces (both physical and digital) and how collaboration informs and shapes a creative process in dance and choreographic practices. The creative period of this research project was informed by somatic explorations between my body, a vacant storefront located on Saint Denis Street in Montreal, and Zoom, which was used to communicate with an artist located in São Paulo, Brazil. The creative research period took place during the COVID-19 pandemic, which prompted me to critically reflect upon notions of time, my creative process, and my routines as a performer and choreographer in the field of dance for 20 years. As such, one of the goals of undertaking a creative process over a period of 30 consecutive days was to set up conditions for a different creative routine to emerge. Four main themes — intimacy, publicness, transparency, and opacity — arose in this process, and each is examined and described in relation to my analysis of the methods used to expand my approach to both collaboration and choreography.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".