Maplet: Integrating Distributed Data Signal Mappings for Performative Interactions Within the Eurorack Modular Synthesizer Ecosystem
Bibliographic record
Abstract
Eurorack is a popular format of modular synthesizers that allow users to customize their instruments by swapping in various modules and creating patches based on their specific needs. While these instruments are very effective at creating a wide range of timbres, the way they in which they are typically performed is not always engaging to an audience or even to the performer. This paper present an initial prototype of Maplet, a module for integrating the libmapper project into the world of Eurorack. libmapper is an open-source project built to support the creation of mappings between components of Digital Musical Instruments. Decades worth of literature has shown how effective mappings are the backbone of the way both performers as well as their audience engage with the output of a digital instrument. With this in mind, we hypothesize that by introducing this mapping-first approach to performing with Eurorack synthesizers that musicians will be empowered to experiment with novel interfaces and techniques for performing with their instrument.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".