Bibliographic record
Abstract
Analyses of tunings have often been carried out independently of pieces in which they are actually realized. Whereas tunings are prima facie relevant to pieces in which they occur, to what extent is this so? And does such a relationship hold in both directions? That is, are analyses of pieces relevant to analyses of their tunings? Both sorts of analysis involve methodological problems and, at least in principle, both sorts of analysis should mesh. Germane to the present discussion are instances of such analytical problems that arise in Central Javanese pélog tunings and in ‘skeletal melodies’ (balungans) of multi-section pieces (gendhings) that employ these tunings. The present account identifies such problems and proposes solutions that attempt to coordinate both sorts of analysis. With regard to tuning per se, relationships among acoustical spectra, pitch determinacy, interval categorization, and ‘errant tones’ are considered. Concerning individual pieces, both jointly and severally, longstanding notions about ‘exchange,’ ‘alternate,’ or ‘substitute’ tones (sorogan), modal identity (pathet), and cadential (gong) tones are addressed. Linking both kinds of analysis—and shared by both—is an amplified formulation of Wertheimer’s Gestalt Grouping Principle of Similarity. Introduced from post-tonal analysis of European-derived music are concepts of common tones, ‘well-formed’ (WF) scales, and interval vectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".