Using corpus studies to find the origins of the madrigal
Bibliographic record
Abstract
A recurring topic in musicology is the origin of the madrigal. Did it come from the frottola, the motet and chanson, or other Italian traditions? MS Florence, BNC, 164-167 (c. 1520) has four sections, each devoted to a different genre: madrigals, other Italian-texted genres, chansons, and motets. These sections provide evidence of genre classification from the period. We encoded the 82 pieces in the manuscript and used jSymbolic to extract 801 features from each file. We then used Weka to train classifiers to identify the pieces in the different sections. This allowed us to test the claims of earlier scholars as to similarity or difference between the madrigals and the other genres. The classifiers could distinguish the other Italian-texted genres from the madrigals only 72% of the time, compared to 100% of the time for the motets and chansons, suggesting that the madrigals are more similar to other Italian-texted pieces than to the other genres. Features based on rhythm were particularly effective in separating the genres, especially in discriminating madrigals from motets.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".