Performing Zimbabwean Marimba: Festivals, Competitions, and Cultural Exchanges from Zimbabwe to the Americas and South Africa
Bibliographic record
Abstract
This thesis explores the current performance practices of Zimbabwean marimba ensembles. I investigate the role of improvisation and the current state of marimba performance practice in Zimbabwe and the USA, how this practice is affected by participation in competitions and festivals, and how Zimbabwean and American marimba ensembles, in turn, influence other marimba ensembles around the world. Previous research on the Zimbabwean marimba traces the development of the instrument from its pedagogical role in the primary and secondary schools in Zimbabwe to playing a key role in professional music ensembles, and also its exodus from Zimbabwe to the United States through the work of Dumisani Abraham Maraire. This thesis begins to address critical gaps concerning both marimba improvisation and the influence of fast growing festivals that have spurred the change that is evident in current performance practices. I illustrate these ideas using two case studies, Winad Musicology (Zimbabwe) and Kutandara Center (USA), tracing their participation in festivals such as the National Institute of Allied Arts (Zimbabwe), the International Marimba and Steelpan Festival (South Africa), Iguazú en Concierto (Argentina), Zimbabwe Music Festival (USA), and Nhemamusasa North (Canada). Findings from the research show that improvisation is, indeed, an integral part of performance in marimba ensemble, and that there is a growing emphasis on the visual aspect of performances in ensembles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".