Feeling the Beat in an African Tone Language: Rhythmic Mapping Between Language and Music
Bibliographic record
Abstract
Text-setting patterns in music have served as a key data source in the development of theories of prosody and rhythm in stress-based languages, but have been explored less from a rhythmic perspective in the realm of tone languages. African tone languages have been especially under-studied in terms of rhythmic patterns in text-setting, likely in large part due to the ill-understood status of metrical structure and prosodic prominence asymmetries in many of these languages. Here, we explore how language is mapped to rhythmic structure in traditional folksongs sung in Medʉmba, a Grassfields Bantu language spoken in Cameroon. We show that, despite complex and varying rhythmic structures within and across songs, correspondences emerge between musical rhythm and linguistic structure at the level of stem position, tone, and prosodic structure. Our results reinforce the notion that metrical prominence asymmetries are present in African tone languages, and that they play an important coordinative role in music and movement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".