Bibliographic record
Abstract
Skips are relatively infrequent in diatonic melodies and are compositionally treated in systematic ways. This treatment has been attributed to deliberate compositional strategies that are also subject to certain constraints. Study 1 showed that ease of vocal production may be accommodated compositionally. Number of skips and their distribution within a melody’s pitch range were compared between diverse statistical samples of vocal and instrumental melodies. Skips occurred less frequently in vocal melodies. Skips occurred more frequently in melodies’ lower and upper ranges, but there were more low skips than high (“low-skip bias”), especially in vocal melodies. Study 2 replicated these findings in the vocal and instrumental melodies of a single composition (Bach’s Mass in B minor). Study 3 showed that among the instrumental melodies of classical composers, low-skip bias was correlated with the proportion of vocal music within composers’ total output. We propose that, to varying degrees, composers apply a vocal template to instrumental melodies.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".