Game art
Bibliographic record
Abstract
For the past decade, virtual reality has been the domain of an elite group of researchers. Artists who wished to explore the medium were forced to form creative alliances with Universities, private research centers such as Interval Research, art centers in Europe and Canada, such as Ars Electronica in Austria and the Banff Centre in Canada, and even the military. But the advent of rapid acceleration in the computer game industry has virtually overnight opened up the artistic potential of real time 3D to a wider array of creators. Personal computer and console hardware can now take on the graphics processing muscle power formerly relegated to high-end supercomputers, and game companies have begun to make their game engines available to a gamers "Modding," "skinning," and "patching" have become popular pastimes for game hackers, but they have also made real time 3D graphics available to a new generation of digital art-makers. These artists are operating independently or in small collectives to create bold new visions for what real time 3D can be as both an artistic medium and a form of popular culture. Much like the early days of video art, this often edgy work is subverting the corporate and mass culture framework of gaming and taking real time 3D to new dimensions. This panel presents a group of emerging artists at the leading edge of the game art movement, each of whom will present recent work and discuss his or her work methodology.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.050 |
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".