Bibliographic record
Abstract
This article is a hybrid reflection on the potential of digital capture to reveal spatio-temporal choreographic negotiations between body and built environment. Acknowledging a lack of engagement within environments that account for the expressive potential of the body and which constrain, dictate and industrialize it, with the proposed somatic research we set out to conduct an observation of the performance of body behavior in the way we interact with a seminal design object: a chair. Thus, the essay is a hybrid observation of bodily performances within quotidian environments that extends choreographic practice and knowledge beyond traditional choreographic contexts to account for the expressive, playful possibilities of the sentient body. The research fluctuates between practices of choreography and design staged in the scenographic landscape of digital photography and digital animation software to map the choreo-mediation between body and object: what is performed by the body in order to ‘interact’ with the proposed choreographic frameworks of a chair. A non-linear, digital, self-ethnographic approach presents two multi-sited studies to analyze this choreomediation: from stop-motion capture of a body performing negotiations with a chair to a digital transcription as a visual mapping of a three-dimensional self-avatar, performing the techniques of sitting.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".