Temporal Transformations In Cross-Cultural Perspective: Augmentation In Baroque, Carnatic And Balinese Music
Bibliographic record
Abstract
To advance any cross-cultural musicology we could do worse than to refine our perspectives on temporality. Yet labeling qualities of musical time–as if such qualities were static–locks in counterproductive essentializations, since old categories like linear and nonlinear time emerged from obsolete distinctions between the West and “the rest” and are based on misleading analogies to the physical world. Such polarized distinctions now seem insufficient. Indeed, any sense of stability in a temporal category is illusory, since even in musics of strict repetition, time and its perceivers are always moving. Thus it may be more productive to typologize temporal transformations, as a way to focus on unfolding process. This article begins to address the question of how many ways musical time can transform. Choosing the culturally and structurally weighted process of temporal augmentation as a case study, I focus on analysis and comparison of examples from Europe, South India and Indonesia. Explanations are sought for how a culturally informed listener perceives the unfolding of augmentation, and in so doing comes to reevaluate the sense of orientation in the music’s time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".