Sense & Sensation: the Act of Mediation and its Effects
Bibliographic record
Abstract
What happens when we mediate? What are the effects of mediation on the body? Are we being altered by technology? This article examines the unique occurrence of cross-model perception found to be associated with moments of acute kinaesonic expression in real-time interactive digital performance. The function and significance of cross-model perception is discussed in the light of the author’s practice with the Bodycoder System and new research into bodily self-consciousness and the sensory effects of new technology within the area of the neurosciences. Empirical findings are framed by a broader theoretical discussion that draws on John Cage’s notion of organicity and Gilles Deleuze’s sensual description of seizure, in opposition to telecommunication’s threat to the morphological stability of reality proposed by Paul Virilio. The article argues that cross-model perceptions, sensory mislocations, compensatory and synaesthesic effects of increasingly intimate and significant couplings of humans and technologies may not, as feared, represent a loss or disruption of perceptual reality, but rather indicate the register of a hitherto unconscious human facility.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".