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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.079

Codex and Gemma teacher scores by category

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

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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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