Stepping back: Reflecting on accessibility in integrated dance improvisation
Bibliographic record
Abstract
Finding more accessible ways to train, create, perform and work is a major concern of researchers and practitioners (Ajula & Redding, 2013, 2014) of integrated and disability dance. In the spring of 2017 eight dancer/researchers from CRIPSiE, an integrated, disability and crip dance company located in Edmonton, came together to investigate their practices of timing through a participatory performance creation process. Participatory performance creation values researcher reflexivity (Heron & Reason, 1997). In this paper I reflect on the way that collaboratively building an improvisation score, a series of tasks and prompts that the dancer/researchers responded to (Gere, 2003), created inaccessibility for one of the dancers/researchers, Robert. At the time I assumed that improvisation itself was inaccessible. Upon reflecting I realized that the improvisation was accessible and that Robert was improvising in ways valued by both the integrated improvisation literature and the other dancers/researchers.
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 | 0.035 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".