29.06.2019 Circus Sessions ii, 2019 Post Performance Discussion about research and development at the Toronto Centre for the Arts
Bibliographic record
Abstract
Michelle Man was principle investigator and facilitator of Circus Sessions 2019, an international Canadian Arts Council funded research and development project that brought together fourteen female artists from five different countries. Collaborating with accessibility facilitator Alex Blumer and the artistic organization Femmes du Feu, the project culminated in two performances at the Toronto Centre of the Arts, Canada. Taking as its starting point notions of conviviality or 'convivencia' in artistic collaborative processes, the embodied research in Circus Sessions took as its foci positive receptivity, hospitality and fascination as creative tools in collective devising processes. Recognizing risk as inherent in circus technique and performance, this project that worked with a select group of mature female artists, sought to question what risks and acts of empowerment might be attached to identity making and artistic expression for the maturing circus artist. By facilitating working environments of conviviality, which resonate with the prevalent concerns in contemporary socio cultural theory (Gilroy, 2004; Wise and Noble, 2016), new ways of recognising with-ness, and negotiating tensions that arose from cultural-artistic difference were found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.412 | 0.168 |
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".