Bibliographic record
Abstract
Research Background: BOX BOX BOX revolves around three self-built portable electronic instruments and foregrounds the unique affordances of DIY technologies while suggesting new aesthetic approaches. Although conceived initially around just one instrument, called “BOX”, two other instruments “HST1d” and “Beat Machine v 0.3” play an important role and continue the DIY agenda. The intention with “BOX” is to communicate the inner workings of the instrument and the decisions/gestures of the human performer via 48 LEDs which are frequently orientated towards the audience. “BOX” has no visible controls on its outside surfaces but hides light and a control interface within, an accelerometer built into the lid acts as overall volume control and provides a link between sound, light, and motion. Research Contribution: The project highlights the tension between tactile gestures and complex remappings; physicality is celebrated but the potential of self-animating systems that are difficult to navigate is explored. There is a clear obsession with circles, loops, and patterns. The overall goal is to foreground a variety of autonomous and manually operated systems that combine to force the performer’s attention to the matters at hand. The software is written in Pure Data and runs on an iPad via Mobile Music Platform, this is controlled by bespoke laser-cut hardware. BOX BOX BOX is an inherently improvisational work but is structured by interactive behaviors and sonic systems. Research Significance: In 2019 BOX BOX BOX was 1. accepted to the Glasgow Electronic and Audiovisual Media (GLEAM) Festival and performed at Glasgow University (UK) 2. presented as part of an Electronic Music concert and invited artist talk at Derby University (UK) 3. the various BOX BOX BOX were presented in a Lecture/Demonstration at the Orpheus Research Summit at the Orpheus Institute in Ghent (BE) 4) presented (somewhat informally) at the Emily Carr University of Art and 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.860 | 0.742 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".