Audiokinetic jukebox (Faster ride in a faster machine)
Bibliographic record
Abstract
BACKGROUND. In the original dance production (not previously reported for ERA), I collaborated with choreographer Garry Stewart, Canadian roboticist Louis-Philippe Demers, UK video artist Gina Czarnecki and London based costume designer Georg Meyer-Wiel to create a startling and unique world. Situating humans in communion with 30 robotic machines and prosthetics, Devolution explored the relationship between robotic and human performers within an artificial ecosystem. Imbued with ritualised process Devolution explored mutualism, territoriality, parasitism, predation, symbiosis and senescence to suggest that in the midst of technology we remain subject to the instincts of the flesh. SIGNIFICANCE: Ruby award winner for innovation, Helpman award for Best new work.Most popular Australian Dance Theatre show. Sellout Adelaide Festival & Sydney Festival seasons. CONTRIBUTION. Melbourne Now provided an opportunity to recontextualise a fromerly multisensory context for composition into an audio only experience. By removing the extrinsic logic (dance, robots, light) which peviously informed the composition the music inhabits a stranger, more seductive, yet confronting world. This installation allowed me to build on research I was undertaking in the Audokinetic experiments lab - namely, How is sound experienced as part of a knot of a multimodal event, and how might it be perceived when the structures which had previously given it context and logic are removed? Results saw the decontextualised experience become richer (through imagination) whilst stranger (in the absence of extrinsic logic).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.260 | 0.065 |
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".