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Record W2293318262

Loss of agency as expression in avatar performance

2014· book· en· W2293318262 on OpenAlexaff
Ben Unterman, Jeremy Turner

Bibliographic record

VenueETC Press eBooks · 2014
Typebook
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGestureAvatarAgency (philosophy)Expression (computer science)Context (archaeology)Focus (optics)PsychologyAestheticsHuman–computer interactionSociologyComputer scienceArtArtificial intelligenceHistory
DOInot available

Abstract

fetched live from OpenAlex

This chapter examines the role of agency as a tool for non-verbal communication in avatar performances staged in online multi-user virtual environments. These live online events have remediated established traditions of performance art and theatre practices into online spaces such as UpStage and Second Life. As with traditional performance forms, particular gestures within virtual worlds are also viewed by the local community as artistic. These gestures are frequently more abstract or codified than everyday gestures, changing their expressive value and significance. The authors focus on two avatar performances Lines (2009) and Spawn of the Surreal (2007), and explore the ways in which audience agency was manipulated in the creation of aesthetic experience. These artistic events demonstrate the strength of agency as an expressive tool and indicate an intriguing new direction for NVC research even outside of the performance context.

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)0.001
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.009
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.028
GPT teacher head0.283
Teacher spread0.255 · 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
GenreOther

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

Citations1
Published2014
Admission routes1
Has abstractyes

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