Thank you Jah Jah fe give me this a colour: Yellowman's revalorization of the<i>dundus</i>
Bibliographic record
Abstract
In 1982, Winston ‘Yellowman’ Foster rose to prominence as Jamaica's king of dancehall reggae and popularized the music genre internationally in the wake of Bob Marley's death. As a dundus, or black person with albinism, Yellowman challenged colonial-derived Jamaican social codes that questioned his blackness and masculinity. By using white society's stereotypes of black hypersexuality and symbols of blackness derived from Rastafari and its ideological forebears, Yellowman was able to transform the dundus identity by portraying himself as African, black, and included in what Carnegie in ‘The Dundus and the Nation’ (1996) calls the imaginary racially homogeneous (i.e. black) Jamaican nation. Furthermore, through his performance of slack or sexually themed songs Yellowman contested embedded cultural definitions of the dundus as impotent and instead successfully represented the body with albinism as the sexually desirable ‘modern body.’ This paper uses interpretive methodologies from interrelated fields such as cultural studies, religious studies and anthropology in recognition that the context of Yellowman's racial critique is found not only in his songs but in his life story as well. Therefore it draws on ethnographic fieldwork, textual analysis of song lyrics and a study of the discourse on Yellowman in the popular and scholarly literature.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".