The visual regime of augmented reality art: space, body, technology, and the real-virtual convergence
Bibliographic record
Abstract
My research investigates the aesthetic and perceptual dimensions of Augmented Reality (AR), one of the most fertile fields of technological innovation and artistic exploration within post-desktop philosophy and practice.Although AR has seen rapid expansion and development, it remains largely undertheorized in the humanities.In this sense, my study contributes to the definition of AR, the identification of the most relevant artworks, and the systematization of a theoretical framework for the field.AR is defined as a set of visualization and interaction systems that permit the perceptual overlay of virtual information (including photos, videos, 3D graphics, texts, sounds) on top of our material reality, in real time, site-specifically, and in an interactive manner.AR's capacity to articulate a hybrid space that seamlessly merges real and virtual elements is what I call convergence.AR artworks such as Jan Torpus' Living-Room 2 (2007), Janet Cardiff and George 17 Art historian Christine Ross writes that "the key rule underlying AR sites is interactivity-they are the very site of affirmation of an inter-agero ergo sum ('I interact, therefore I am')."Christine Ross, "Augmented Reality Art: A Matter of (non)Destination," Proceedings of the Digital Arts and Culture Conference (2009), After Media: Embodiment and Context, University of California, Irvine.Online at http://escholarship.org/uc/item/6q71j0zh (accessed December 2015).18 Media theorist Jason Farman notes that AR is able to "transform our location into an information interface."Jason Farman, Mobile Interface Theory.Embodied Space and Locative Media (New York and London: Routledge, 2012), 13. 19 Here I use the term paradigm, following philosopher Thomas Kuhn, in the sense of a set of particular beliefs and knowledge that are regarded as "established" and the paradigm shift as the moment when "one conceptual world view is replaced by another."Thomas Kuhn, The Structure of Scientific Revolutions, Second Edition, Enlarged (Chicago: The University of Chicago Press, 1970 (1962), 10. potential should be mentioned at this point.One is Historypin (launched in 2011), a digital, usergenerated archive of historical photos, videos, audio recordings and personal recollections made 43 See Daniel Wagner et al, "The Handheld Augmented Reality Project," Christian Doppler Laboratory, Graz University of Technology, 2005-2007,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".