Rainboard And Musix: Building Dynamic Isomorphic Interfaces
Bibliographic record
Abstract
"Since Euler's development of the Tonnetz in 1739, musicians, composers andinstrument designers have been fascinated with the concept of musicalisomorphism, the idea that by arranging tones by their harmonic relationshipsrather than by their physical properties, the common shapes of musicalconstructs will appear, facilitating learning and new ways of exploringharmonic spaces. The construction of isomorphic instruments, beyond limitedsquare isomorphisms present in many stringed instruments, has been a challengein the past for two reasons: The first problem, that of re-arranging noteactuators from their sounding elements, has been solved by digital instrumentdesign. The second, more conceptual problem, is that only a single isomorphismcan be designed for any one instrument, requiring the instrument designer (aswell as composer and performer) to ""lock in"" to a single isomorphism, or tohave a different instrument for each isomorphism in order to experiment. Musix(an iOS application) and Rainboard (a physical device) are two new musicalinstruments built to overcome this and other limitations of existing isomorphicinstruments. Musix was developed to allow experimentation with a wide varietyof different isomorphic layouts, to assess the advantages and disadvantages ofeach. The Rainboard consists of a hexagonal array of arcade buttons embeddedwith RGB-LEDs, which are used to indicate characteristics of the isomorphismcurrently in use on the Rainboard. The creation of these two instruments /experimentation platforms allows for isomorphic layouts to be explored in waysthat are not possible with existing instruments."
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".