Bibliographic record
Abstract
BACKGROUND Recent developments in so-called 'augmented reality' artworks and digital virtual communities have identified a need for research into the nature of the relationship between the digital and 'real' worlds. CONTRIBUTION The work, Reproduction, by Adam Nash and John McCormick is an examination of the nature of the relationship between digital, virtual entities and human interactors via the means of motion- and data-capture forms. It continues their ongoing experimentation in audiovisual, performative, evolving, virtual environments. This practice-led research project consists of an immersive, whole-room audiovisual projection environment that allows human visitors to engage in a symbiotic improvisatory interaction with digital entities that evolve according to their 'own' digital environmental conditions as well as their interactions with human visitors. Innovatively, the work highlights the ability for digital works to transcend a simple virtual/actual dichotomy and explore new realms of generative interaction that transform equally the digital and physical, with neither side taking precedence, thus establishing a collaboration between human and digital entities. It encourages a contemplative navigation and engagement with the virtual realities. SIGNIFICANCE As an indicator of its significance, the work was originally developed at an Australia Council-funded Artist-in-Residency at Ars Electronica Futurelab in Linz, Austria, the world's leading venue for leading-edge digital and media art. The work was subsequently further developed at a residency on invitation at Neutral Ground Gallery in Canada. Finally, it was selected by a committee comprised of artists, writers and curators for exhibition at Screen Space Gallery, one of Melbourne's most significant galleries exhibiting digital and interactive art.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.681 | 0.445 |
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".