Genome of melody: applying bioinformatics to study the evolution of Gregorian chant
Bibliographic record
Abstract
Gregorian chant was a central musical tradition in Medieval Latin Europe and one of the expressions of its cultural unity: any Latin Christian liturgy, such as the weekly Sunday mass, would have involved Gregorian chant as a major part of the prescribed ritual. The Gregorian legend of chant melodies' divine origin required the practitioners to conserve them, to the extent that this requirement motivated the development of exact pitch notation. Nevertheless, surviving manuscripts document a considerable melodic diversity. Some systematic patterns within this melodic diversity have previously been observed in chant scholarship, especially during efforts to build a critical edition reconstructing the earliest possible forms of chant melodies with philological approaches. Taking an evolutionary perspective, we notice analogies between biological evolution and processes of chant transmission, which lead us to suggest recovering these 'melodic dialects' using phylogenetic methods instead. In this paper, we show that phylogenetic models recover historically plausible patterns of chant melody evolution. We observe that some, but not all, institutional networks play a more important role than geographical proximity. Phylogeny is shown to be a viable class of methods for studying chant melody, and we discuss next steps for a more comprehensive evolutionary approach to chant. This article is part of the theme issue 'Transforming cultural evolution research and its application to global futures'.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".