Mizrahi Rap in Israel: Ethnicity and Intertextuality in the Cosmopolitan Post-Genre Era
Bibliographic record
Abstract
Abstract This article uses ethnography and music analysis to compare the intertextuality of Mizrahi music and rap by two generations of hip hop artists in Israel. The perceived compatibility of the two genres is often motivated by apparent similarities between Mizrahiness and Blackness as two ethno-racial categories associated with disenfranchisement. We argue that varied musical approaches to combining Mizrahi music and rap reflect the ways that different generations have negotiated local and global cultural spaces. Early rappers in Israel, who regarded Mizrahi music and rap as two distinct genres, tended to compose a rather mechanistic juxtaposition between them that suggests a perceived ontological gap between the local and the global. Conversely, contemporary rappers have been socialized in and make use of a virtual and borderless musical environment, a realm that arguably breaks down the logic of coherent musical genres and the cultural and spatial compartments they once seemed to occupy. This is reflected in their typical compositional style, which merges the two genres into a seamless, indistinguishable form. The case of “Mizrahi rap” demonstrates how global trends are inscribed into local negotiations of ethnicity and how the perceived relatedness between ethnic and racial categories (i.e., Mizrahiness and Blackness) defines new conceptions of the global sphere 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".