Very Nervous System and the Benefit of Inexact Control. Interview with David Rokeby
Bibliographic record
Abstract
The Canadian artist David Rokeby (1960) has been creating interactive sound and video installations since 1982. His work directly engages the human body or involves artificial perception systems and intends to explore time, perception, issues of digital surveillance and the relationships between humans and interactive machines. In 1982 Rokeby started developing Very Nervous System, a real time motion tracking system, which monitors the user's action via video camera, analyses the data in the computer and responds to the interactor's input. On the basis of this system - which is also used in music therapy applications and as an activity enabler for victims of Parkinson's Disease - Rokeby created several interactive installations with real-time feedback loops using video cameras, image processors, computers, synthesizers, and sound systems. Rokeby has graduated with honours in Experimental Art from Ontario College of Art in 1984, he has exhibited and given talks in Canada, US, Mexico, Brazil, Germany, Austria, France, Italy, Belgium, Finland, Japan and Korea, including the Venice Biennale in 1986, Ars Electronica (Linz, Austria) in 1991 and 2002, the Mediale (Hamburg, Germany) in 1993, the Biennale di Firenze (Florence, Italy) in 1996 and the Venice Architecture Biennale in 2002. Rokeby was, among others, awarded the Petro Canada Media Arts Award (1988), the Prix Ars Electronica Award of Distinction for Interactive Art (1991 and 1997), and the Award for Interactive Art of the British Academy of Film and Television Arts (2000). Roberto Simanowski talked with him about "systems of inexact control" which reject the control fetish, about their pragmatic role in every day life, about the bastardization of aleatoric art, about interactivity as the decline of critical distance and about technology as a genre.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.021 |
| 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".