<scp>Alain-Philippe Durand</scp> (ed.), <i>Black, blanc, beur: Rap music and hip-hop culture in the francophone world</i>. Lanham, MD: Scarecrow Press, 2002. Pp. xvii, 150. Cloth $49.50, Paper $24.50.
Bibliographic record
Abstract
This book is a collection of 10 articles treating different aspects of francophone rap music and hip-hop culture. Despite the facts that the first French rap recordings date from 1984 and that France has since become a center of rap music second only to the United States, this is the first book devoted to the subject. André Prévos begins the volume with a history of French rap music from its origins through the 1990s. Here we are introduced to many of the names that appear in later chapters. The following chapters treat the regional specificity of Marseilles rap (Jean-Marie Jacono); rap audiences in Marseilles (Anthony Pecqueux); the politics of French rap (Paul Silverstein); rap as a social movement (Manuel Boucher); hip-hop as an aesthetic subculture (Anne-Marie Green); an ethnography of tagging (Alain Milon); hip-hop dance (Hugues Bazin); rap in Libreville, Gabon (Michelle Auzanneau); and rap in Québec (Roger Chamberland). All the chapters except those by Prévos, Silverstein, and Chamberland were translated from French.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.048 |
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".