Accidentals in the mid-fifteenth century : a computer-aided study of the Buxheim organ book and its concordances
Bibliographic record
Abstract
The Buxheim Organ Book, the largest fifteenth-century manuscript of keyboard tablature, has never before been examined as a whole in light of musica ficta issues, although it contains far more accidentals than any contemporaneous source in mensural notation. Although tablature has been used by various scholars to examine accidentals in sixteenth-century music, studies of fifteenth-century accidentals have focussed on theoretical evidence and small groups of pieces from mensural sources. The author uses the Buxheim Organ Book to extend the investigations of accidentals in tablature back into the fifteenth century, combining the large data set provided by this manuscript with a statistical approach modelled on that of Thomas Brothers's smaller-scale study of the chansons of Binchois. Specialised computer programs are introduced, which detect musical structures relevant to the analysis of Renaissance music such as different types of cadential voice leading. These programs function as extensions to David Huron's Humdrum Toolkit. With these tools, signing practises in the intabulations are statistically compared with all of the concordances of the models. Conclusions are suggested pertaining to issues of signature accidental transmission, partial signatures, mode, and musica ficta, which can be used as a contextual backdrop for the analysis of individual pieces. The evidence provided by the accidentals in Buxheim and its concordances draws a clear picture of how a group of fifteenth-century musicians added accidentals to polyphonic music. For the first time, this study provides us with principles and guidelines for musica ficta -decisions based on actual practice.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".