Bibliographic record
Abstract
Research Background Soundtracks is an exploration of computationally mediated rotation using motorized magnets to create modular ‘soundtracks’. Three industrial motors are programmed using Arduino; each motor propels magnets that inscribe, over time, physical ‘tracks’ into the surface of a wooden board coated in layers of paint. The motion is visceral but small-scale. The sound-world revolves around a series of ‘scrapes’, but surprising sonic details and resonances emerge. The direction and speed of rotation change frequently while the magnetic forces add a layer of complexity. Silence and stillness occur often, these force the perceiver to pause and contemplate gallery sounds that are often considered ‘background’. Research Contribution This project explored the circular information design of Manuel Lima and is inspired by the kinetic sculpture of Arthur Ganson and Zimoun. The goal was to develop long-duration work that develops over time (texture of the painted surface functions as a musical score that changes as various layers of paint are worn down). The work is envisaged as a musical composition that relies on electro-mechanical means, the power of magnetism, and the acoustic properties of the chosen materials (no loudspeakers involved). However, it also involves algorithms that generate pseudo-random results, so is inherently digital as the magnetic/mechanical forces are augmented by machine agency. This project foregrounds magnetic forces/motion and uses this to draw perceivers in close; once close to the ear the auditory sense takes over and the role of sound in a gallery context is highlighted. Research Significance This project is a collaboration with Dr Peter Bussigel (Assistant Professor/Intermedia artist). Soundtracks was developed during a two-week artist residency (Department of New Media + Sound Arts) for a four-week exhibition (October 25th-November 29th 2019) in the ~Diffuser Gallery at Emily Carr University of Art + Design in Vancouver (CA).
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.036 |
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".