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Record W2287174670

Embodying Kinaesthetic Stimulants in a Technological World, A Kinaesthetic Exploration of Western Technology's Affect on the Body

2014· dissertation· en· W2287174670 on OpenAlexaboutno aff
Michelle Darlene Grace McClelland

Bibliographic record

VenueYorkSpace (York University) · 2014
Typedissertation
Languageen
FieldArts and Humanities
TopicArt, Technology, and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsChoreographyMovement (music)Affect (linguistics)PsychologyVisual artsDanceAestheticsArtCommunication
DOInot available

Abstract

fetched live from OpenAlex

This thesis addresses the potential kinaesthetic influences technology has on the body and how these influences can be used to extract original choreography. Based on Gretchen Schiller’s assertions that the body’s interactions with technology “contribute to the range of one’s movement repertoire and kinaesthetic condition” (Schiller 109), this research purports that the body’s interactions with transportation technology (specifically trains, subways, and automobiles), hand-held technology (cell phones, video games, and electronic children’s toys), online networking, and the television, affect its kinaesthetic condition. This is achieved through the body’s experience of new shapes, tensions, and weight-holding patterns. The individual experiences of urban Western bodies are specifically researched, particularly those in Toronto, Canada. Through site-specific movement explorations, this thesis argues that a heightened kinaesthetic awareness allows a choreographer to extract technological qualities and create original choreography. This process will, in turn, widen the choreographer’s awareness to other kinaesthetic movement inspirations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.230
Teacher spread0.200 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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