Bibliographic record
Abstract
Classical meter theory, derived from European, notation-based musical practice, requires notionally absolute isochrony. This article proposes a more flexible concept of rhythmic frequencies (or periodicities) represented by continuous functions over time, and develops rhythmic theory from it that is more global in scope. A rhythm is a good fit to a given frequency if its onsets are close to peaks of one of these functions, without having to precisely coincide. This provides some useful tools for understanding properties of rhythms and how different rhythms interact, including the rhythmic spectrum which shows all the frequencies present in a rhythm. Maximally even rhythms like the African standard pattern and tamborim rhythm of samba, are those which maximize a given frequency for a given grid, and often function as basic rhythms (e.g. “timelines” or claves) in many musical traditions, as do other rhythms, like the “Bo Diddley” rhythm and Clave Son, with strong representation of a single frequency. When rhythms expressing nearby frequencies are combined, they interact to produce slow phase shifts over longer cycles, a feature of timeline rhythms, and also more complex isorhythmic designs in, for example, the late music of György Ligeti, and recent jazz compositions by Dave King and Miles Okazaki.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".