Bibliographic record
Abstract
This paper discusses de Clercq’s contribution to our understanding of the relationship between scale degree and cadence type within Bach chorales from the perspective of style and probability. De Clercq is applauded for the diligence of this research and for attempting to synthesize findings into a practical, working model of benefit to music-theory students and educators. A literal interpretation of a premise underpinning his model—that more common musical events are more indicative of a style—is, however, found to be inconsistent. A test is described in which university students enrolled in a second-level harmony class were presented with pairs of cadences. Cadences were manipulated in various ways, primarily to investigate whether the inclusion of certain figurations would result in a perfect-authentic cadence—the most ubiquitous cadence within Bach chorales—being considered less stylistic than a never-occurring cadence. This proved to be the case, demonstrating the importance of figuration over scale degree and cadence for the accomplishment of style. De Clercq’s model is further discussed with respect to probabilistic models of music and in relation to proscriptive approaches to teaching harmony.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".