Wrecking rap's conventions: the cultural production of three daring Detroit emcees
Bibliographic record
Abstract
Abstract This article profiles the music of three politically motivated hip hop emcees. It combines textual and musicological analysis with ethnographic data to examine the ways in which these women use music to empower themselves and to contribute to meaningful, positive change in post-industrial, post-bankruptcy Detroit. These narratives are significant because they combat the dominant, hegemonic two-dimensional representations of African American women that are epitomised in commercial hip hop and popular culture at large. Further, in a context where art and activism are connected, their work challenges the current controlling images and sexual scripts that dominate both commercial music industry representations and scholarship on women in hip hop. The artists we profile exemplify a new kind of musical movement where women are agents and creative solutionaries. At times, they are explicitly critical of the narrow range of black womanhood presented in popular culture and in other instances, they focus on issues such as the environment, race relations, racialized bodies, poverty and abuse, all the while challenging the hip hop industry and popular culture norms that communicate who black women are and who they should be.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".