Break-in’ Point: Somatic narratives: The convergence of arts and science in the transformation of temporal communities
Bibliographic record
Abstract
Break-in’ Point, a 2012 arts and science performance and community engagement research initiative, was presented in the spring and fall semesters at the University of Leeds in the United Kingdom at Stage@Leeds. The outcome of a collaboration between dance artist A3 and theoretical physicist A2, under the direction of performance researcher A1, Break-in’ Point is based on a series of real-life encounters at intersections of arts and science – exploring force, risk, exposure and resilience. The Break-in’ Point performance offered an interrogation of the critical point at which physical, mental, and/or emotional strength give way under stress – causing structural degeneration and the experience of what lies beyond. This article is an examination of the performance, reviewing and analysing it as an imagined somatic zone – embodied encounters that transcend temporal bound-ness, compelling and igniting new possibilities – that engaged spiritual and epistemological transformation of performers and audiences. The article addresses three main periods in the life of Break-in’ Point: (1) the development period – script building and rehearsals, (2) the performance – live encounters between and among performers and audiences and (3) beyond the theatre – digital engagements in the classroom and pedagogy. The article contributes new concepts and new ways of thinking about science education, the role of digital technology in pedagogy, dance/theatre public engagement and community arts practices as practices of healing, health and resilience.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| 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".