Semiotics, HCI and the Avant-Garde
Bibliographic record
Abstract
Ambience and immersive technological environments allow us to explore some basics of human pragmatics that lie beyond linguistics, intentionality and the subject-agency perspectives of human interaction.We focus on gesture and the body in sense-making and propose a discussion drawing on a nondualist and agent-free account of embodied, material experience.By agent-free we mean an approach that does not presume the subject.Moreover, we deal with the problem of intersubjectivity by studying the human coordination of activity without appealing to a transmission theory of communication.(Harris, 1997) We achieve this by considering how gesture spans multiple bodies and how aesthetic design works with this and facilitates it.The paper is in two parts, the first part covers movement studies, focusing on gesture and body movement, drawing on the acting and pragmatics, and the second part develops this with the example of the TGarden, a responsive play space for experimental performance augmented by gesturally nuanced computational media.We ask the following questions: how do people collectively and individually improvise meaningful gestures in a TGarden environment?How can we build environments in which people can become more virtuosic in their performance with continued play?How can people coordinate powerful experiences without appealing to verbal language or to a linguistic representation?In order to sustain such improvisatory but non-random play, TGarden is built explicitly from metaphorical, dense tangible material substrates and field-based rather than object-based or agent-based responses to gesture and movement.These material substrates include live, gesturally parameterized projection video, gesturally modified sound, and image-bearing or sensate fabrics.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".