Towards a Generative Approach in Understanding the Kónkóló Timeline in Yoruba Music
Bibliographic record
Abstract
The kónkóló timeline is ubiquitous in most Yoruba musical practices; serving as the background rhythmic pattern and time marker, it is the principal pattern that delineates the music’s rhythmic structure. Previous bodies of work have investigated the nature of Western rhythm from a range of different perspectives, such as in terms of cultural significance, cognitive and neural relationships with language and movement, and potential pedagogical and therapeutic value. There is also increasing interest in the connections between formal and traditional semantic approaches to analysing musical meaning, including for rhythmic structures. The current, interdisciplinary study attempts to bring together aspects of these distinct areas of knowledge, through a generative approach, in understanding musical and cultural functions of the kónkóló timeline in Yoruba music. This is attempted by seeking to understand the creative ways in which the kónkóló timeline is used and communicated between the musicians, dancers, and audience in Yoruba music. While this study is based on a generative approach in Yoruba traditional music, which is unlike the generative theories of Western tonal music, it agrees to the presence of a hierarchical system of metrical organization and rhythmic grouping in the music. Data were elicited during fieldwork through observation and interviews of traditional drummers and dancers. The study provided information on the kónkóló timeline’s metric/rhythmic structure as unique inputs, and the predictive outputs in the music, such as specific syllabic and rhythmic generation, movement and dance patterns, and other socio-cultural layers that constitute the full experience of Yoruba music. This also include perspectives on the social and affective features of kónkóló patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".