Another lesson from Lassus: using computers to analyse counterpoint
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The authors report on experiments they have run using the computer to search a small corpus of Renaissance pieces (the famous Lassus duos of 1577) for recurring contrapuntal combinations. They liken these combinations (or ‘modules’ as Jessie Ann Owens has called them) to words in a text, and the process of finding them, to work done by linguists such as John Sinclair on large corpora of text. The program used was devised by a team at McGill University as part of the ELVIS (‘Electronic Locator of Vertical Interval Successions’) project. The interval successions are identified by the vertical intervals and the melodic motions that connect them, in the manner of Tinctoris’s counterpoint treatise (1477), which illustrates most of the possible ways two vertical intervals can be connected. The authors find that some short interval successions appear, as we would expect, in repetitions of thematic material (i.e. as parts of soggetti associated with specific text phrases). Others, however, occur in apparently run-of-the-mill counterpoint: in the middle of words, in the middle of melismas, across phrase boundaries and embellished in a variety of ways. These often exhibit surprising consistency as to semitone position and possible modal associations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it