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Record W4400067059 · doi:10.1145/3658852.3659066

Participative Robotics and The Spectacle of Motion Capture

2024· article· en· W4400067059 on OpenAlexaff
Louis-Philippe Demers, Bill Vorn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicEmbodied and Extended Cognition
Canadian institutionsConcordia University
FundersUniversitas Brawijaya
KeywordsSpectacleRoboticsArtificial intelligenceMotion captureMotion (physics)Computer visionComputer scienceRobotHuman–computer interactionPolitical science

Abstract

fetched live from OpenAlex

Experiments with animating human bodies on stage are reported here. In our performances, animations are carried out by augmenting human motions, coercing actions, experiencing disruptions of realism, shifting kinesthetics, or altering anatomies. These activities are performed on the participants' bodies using mechatronic devices such as exoskeletons, supernumerary limbs, or symbiotic apparatuses. We adapt motion capture (mocap) techniques and data gathered from bodies, crowds, and devices to control, augment, and alter human movements as if we were animating 3D virtual characters, yet with the inevitable constraints of the physical world and human body integrity. In a constant shift of locus of perception, through 'ghost' control and gestural doubles, human volition is destabilized and turns into an uncanny paradox of pleasure and loss of self-control.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.035
GPT teacher head0.280
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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