Bibliographic record
Abstract
REPLICAS uses text messages from couples at various stages in their relationships as inspiration for dance duets, exploring how people communicate digitally. Through practice-based research, Essex explores issues raised by Hayles’s work on deep versus hyper attention (Hayles, 2016). Raw text messages, videos and photos are sent to audience members while they are encountering live performance work, creating a complex relationship between digital and live presences. This practice-based research into attention and how it can be diverted from the live by the digital, is informed by Home-Cook’s writing on attention-stretching in information-rich environments. REPLICAS examines questions at the forefront of our culture: How is communication changing in the digital age? What can be lost/gained by our new intimacy with technology? How is our attention being stretched by this new information-rich environment? What is the current role of physical presence? This work continues Essex’s research on methods of communication and how dance can provide an opportunity for the embodiment of complex emotions. During the rehearsal process, text messages from subjects were categorized using the Big Five Personality Traits (Tupes, Christal and Goldberg) and the physicalising of these traits was developed through practical research with dancers. REPLICAS (originally titled Distance Duet) is the culmination of three years of research and two ACE grants. It was commissioned by the Stockton International Riverside Festival (SIRF), with support from Dance City and Pavilion Dance South West. It was performed at SIRF and MIMA. The methods for physicalising the Big Five Personality Traits developed through this practical research have influenced practice, with Essex being invited to share her research at the Young Lungs Dance Exchange (Canada) and Animex (Middlesbrough). The project was selected for Respond_, a new digital platform, formed around the work of Liz Lerman’s ‘Critical Response Process’ (CRP), developed by Yorkshire Dance, University of Leeds and Breakfast Creatives.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.579 | 0.380 |
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".