Intersections of Gender, Race, and Genre: Cammie Gilbert and Black Female Subjectivity in Metal Music
Bibliographic record
Abstract
ABSTRACT This article illustrates a framework for intersectional music analysis that is based on four domains of oppression understood by Patricia Hill Collins as the matrix of domination. Arising from black feminist thought, intersectionality responds to intersecting oppressions imposed upon individuals. An intersectional perspective illuminates multiple layers of identity by attending to issues of race, gender, sexuality, class, ability, and other factors. By transferring Collins’s matrix of domination to cultural contexts, we can understand how intersectionality applies to the cultural forms that arise. We might offer a critique of the music industry and its gatekeeping practices, and yet to understand the significance of systematic forces of oppression we must also look to musical genre discourse for its workings of social oppression and cultural distinction. To illustrate, I analyze a recording by Oceans of Slumber, a Texan progressive metal band that is fronted by black female vocalist, Cammie Gilbert.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".