Bibliographic record
Abstract
Around the world, music theory is commonly understood as knowledge cultivated by specialist musicians and scholars.It is also assumed to require analytical terminology supplemented with visual representations.However, even in performance traditions less concerned with explicit formulations of musical processes, musical theorizations can be embedded in the minds of the performers and their audiences.This article aims to broaden common understandings of music theory through the lens of orally transmitted ritualistic verses from Sri Lanka, which are sung or recited along with drumming.Versions of these Sinhala verses have been previously published in Sedaraman, J.E. 2008 [1964].Uḍ araṭ a Näṭ um Kalāva [The Art of Upcountry Dance] (Colombo: M.D. Gunasena).Transliterations and translations of these verses are presented alongside audio recordings of performances (some including video), to illustrate the ways in which the oral transmission and performance of such lyrics involves different modes of theorizing-such as knowledge of how conventional poetic meters can be sung, shared understandings of how to interpret vocables as drum strokes, and the association of different drum timbres with cosmological references.The article provides socio-historical context for the verses, details their form, explains the underlying conventions of versification, and analyzes the relationship between recited syllables and drum strokes, while highlighting the various processes of theorizing that undergird them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".