Contemporary Art and Virtual Reality: New Conditions of Viewership
Bibliographic record
Abstract
The article aims to respond to the lack of studies on the relationships between contemporary visual arts and VR, focusing on the role of “storytelling” and identifying what distinguishes VR art projects from other contemporary VR uses, namely their criticism of the VR medium itself. VR has developed a new language in the last five years, based on specific visual grammar and allowing new narration forms. Visual artists have been attracted to VR in search of new modes of production and expose the negative impact of technology in our perception of reality, or else the new mediated ways of seeing and distanced interaction with the world around us. The first part is dedicated to the discussion of Canadian artist Jon Rafman’s View of Pariser Platz (2016) and American artist Jordan Wolfson’s Real Violence (2017), two of the first Oculus Rift-based art installations to develop a metalinguistic commentary on how VR, although promising immersion, produces, in fact, alienation, homogenization, brutalization and the loss of empathy. The article continues with a discussion on the recent rise of tech companies aimed at the production of contemporary artworks based on VR technology: Acute Art (London), Khora Contemporary (Copenhagen), and VIVE Arts (Taiwan). This is a new expanding field that is changing the ontology of artmaking and redefining the artist's role, mainly in light of the cooperation with technicians and programmers.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".