AUTOMATIC MOTIVIC ANALYSIS INCLUDING MELODIC SIMILARITY FOR DIFFERENT CONTOUR CARDINALITIES: APPLICATION TO SCHUMANN'S OF FOREIGN LANDS AND PEOPLE
Bibliographic record
Abstract
This paper aims at presenting a topological model of motivic structure and analysis, and its application, via the implementation, to Schumann's Of Foreign Lands and People in Scenes from Childhood.Our immanent approach importantly includes the concept of contour similarity for different motif lengths making then possible to formalize the germinal motif (or leitmotif) concept.Based on motif, contour, gestalt, and motif similarity concepts, the crucial step in our mathematical model is indeed the introduction of neighborhoods of motives that include (similar) motives of different cardinalities and that yield a topological (T 0 )-space on the set of all motives of a composition.In this space, the 'most dense' motif corresponds to the piece's 'germinal motif'.The model implementation (JAVA) constructs the spaces and in particular calculates, for each similarity threshold (neighborhood radius), the germinal motif; this is graphically represented in motivic evolution trees.The application to Of Foreign Lands and People briefly exemplifies our method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".