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Record W2150821050 · doi:10.1177/1354856513514333

Working towards defining an aesthetics of augmented reality

2014· article· en· W2150821050 on OpenAlexaff
Helen Papagiannis

Bibliographic record

VenueConvergence The International Journal of Research into New Media Technologies · 2014
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsYork University
Fundersnot available
KeywordsAestheticsAugmented realityWork (physics)Transition (genetics)SociologyEveryday AestheticsArtEpistemologyComputer sciencePhilosophyHuman–computer interaction

Abstract

fetched live from OpenAlex

The present is a critical time in augmented reality's (AR) definition, as a new medium with aesthetics and conventions just beginning to emerge. We are at a moment when we can look both to the future and to the past: still seeing the previous forms that are shaping AR as a medium while paving new paths, contributing to novel styles and tropes. This article will work toward defining an aesthetics of AR as a new medium in transition, discussing my work as both an artist and a researcher in AR.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0050.034
Scholarly communication0.0180.016
Open science0.0020.007
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.124
GPT teacher head0.399
Teacher spread0.276 · 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 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

Citations9
Published2014
Admission routes1
Has abstractyes

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