Bibliographic record
Abstract
This paper introduces a novel approach to rhythm-based contemporary music composition. I propose that recent insights into the cognitive limits of rhythm and meter can be translated into a compositional framework that considers not only the structural and abstract properties of rhythm structures, but also their perceptual impact on an audience. This method enables the preservation of a high degree of structural complexity while enhancing its perceptual effectiveness and minimizing notational intricacy. The approach is facilitated by the use of OpenMusic, a computer-assisted composition software, alongside a visual representation of metric modulation networks that I call a rhythm lattice. To illustrate this approach, I present two examples from my recent chamber music works. Notes on Contributor Louis-Michel Tougas is a composer, percussionist, and producer based in Montréal, Québec. His doctoral research at McGill University explores the cognition of polyrhythm and the role of timbre as a form-bearing dimension of music perception. He has taught computer-assisted composition and 20th-century compositional techniques at McGill, and holds degrees in composition and analysis from the Conservatoire de musique de Montréal and the Hochschule für Musik Stuttgart. His music has been performed internationally by ensembles including Talea, the Bozzini Quartet, Ascolta, Quasar Saxophone Quartet, Ensemble Éclat, and Quatuor Mémoire.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".