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Reimagining the Audience-Dancer Relationship Through Mobile Augmented Reality

2020· book-chapter· en· W3047640038 on OpenAlex
Patrick Parra Pennefather, Claudia Krebs, Julie-Anne Saroyan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAdvances in media, entertainment and the arts (AMEA) book series · 2020
Typebook-chapter
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDanceAugmented realityDocumentationVisual artsProcess (computing)MultimediaArtChoreographyProduction (economics)SociologyAestheticsComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

The research and development of an augmented reality (AR) application for Vancouver-based dance company Small Stage challenged a team of students at a graduate digital media program to understand how AR might reinvent the audience-dancer relationship. This chapter will chronicle the AR and choreographic development process that occurred simultaneously. Based on the documentation of that process, a number of insights emerged that dance creators and AR developers may find useful when developing an AR experience as counterpart to a live dance production. These include (1) understanding the role of technology to support or disrupt the traditional use of a proscenium-based stage, (2) describing how AR can be used to augment an audience's experience of dance, (3) integrating a motion capture pipeline to accelerate AR development to support the before and after experience of a public dance production.

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.

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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.802

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 studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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