Bibliographic record
Abstract
Susanne Fürniss’s (2006) magisterial survey of Aka polyphony analyzes a remarkable duet in which each singer draws material from a regularly repeating cycle but varies it on the fly to complement her partner’s likewise varying repetitions. This texture of two independently cycling but interacting voices, although well-suited to the Aka’s conception of musical structure, is not unique to them; indeed, examples from many traditional cultures have been recorded. In some instances, the musicians may be heard coordinating their variations to forge large-scale musical form out of what would otherwise be uniform repetition. This paper analyzes three items that illustrate the potential of such equal-voice cyclic duets to support formative interactions of timbre, timing and grouping that are not possible in monophony and not so effective in other polyphonic textures. In a funeral lamentation from the Solomon Islands, the singers’ timbral variations set up and realize large-scale formal articulations. In a flirtatious song of the Ecuadorian Amazon, as the singers repeat irregularly timed cycles at different tempos, they adjust the placement of their respective beats to create phases of greater or lesser synchrony and changing leader-follower relationships. Lastly, in a communal dance of French Guyana, one part adjusts its timing to accommodate the addition and deletion of events by the other, creating an unpredictable, dramatically charged process that they gradually direct towards a stable regular groove. Like the Aka duet, these compositions transform what might be a rote, mechanical procedure into a lively vehicle for distinctive formal and expressive effects.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".