Jamaican Sound Systems and Knowledge Systems: Practice-Based Research (PBR) in Popular Culture
Bibliographic record
Abstract
Working with popular street cultures in the Global South offers scope for practice-based research (PBR) to go beyond its application with creative practitioners in the galleries and theatres of the Global North. We start from an account of a “reasoning session” with reggae sound system owners, selectors, and engineers staged as a PBR event in Kingston, Jamaica. Such popular music cultures across the Global South have their own specialist apparatus for playing recorded music and—most important for a PBR investigation—their own embodied, situated, and tacit knowledge systems. These include the sophisticated arts of selecting music, tuning up a sound system, and the value of the culture for the communities from which they originate, as well as strategies for current challenges, such as police harassment and lack of government recognition or support. Accessing such grassroots knowledge systems requires not only a good rapport with local practitioners but also close cooperation with their own organizations and with local university researchers. Such PBR also demands sharing research findings—for example, by screening the documentary film we made of the reasoning session for its participant. It is concluded that practitioners’ ways of knowing as revealed by PBR can help challenge conventional ideas about the nature of knowledge itself.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".