Crossing the bar lines : : locating 20th and 21st century strategies of musical resistance
Bibliographic record
Abstract
From the 1990s on critical musicologists have used signifyin(g) theory to create a more inclusive, critical discursive that reflects African American ideas and experiences in relation to race, gender, and sexuality as manifested in their musical practices. Because I found this theoretical paradigm incomplete, I explore Afro- pessimism social theory as a way to help explain why African-American musicians use unconventional strategies of music making to forge non-monolithic musical identities that resist hegemonic theories and practices of music. Through data analysis of music compositions, instrumental improvisations, and musician interviews, I analyze how African-American musicians express their racial imaginaries in relation to space, time, center, and periphery. Particularly vibrant data examples were found in the improvisations and compositions of pianists Andrew Hill and Stanley Cowell, trumpeter Ambrose Akinmusire, and the music videos of T-Pain
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.017 | 0.035 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".