Bibliographic record
Abstract
In Music and Discourse, Jean-Jacques Nattiez theorizes that noise is not only subjective, its definition, and that of music itself, is culturally specific: “There is never a singular, culturally dominant conception of music; rather, we see a whole spectrum of conceptions, from those of the entire society to those of a single individual” (43). Noise in this context is therefore most often positioned as the result of music that runs contrary to an established set of rules. However subjective the assessments of both the musical producer and listener, Nattiez notes that these “‘criteria’ are always defined in relation to a threshold of acceptability encompassing bearable volume, the existence of fixed pitches, and a notion of order – which are only arbitrarily defined as norms” (45). If these criteria are arbitrary, then music might just as arbitrarily be redefined to valorize noise rather than eschew it, something true of Public Enemy and their musical aesthetic of noise. It Takes a Nation of Millions to Hold Us Back (1988) uses saxophone samples in many songs that act in tandem with the lyrics to form “an aggression against the code-structuring messages” (Attali 27) found in popular music conventions. Moreover, noise has been a part of the musical landscape for longer than we might think. Public Enemy’s examples are comparable to the use of dissonance in the music of jazz legends Duke Ellington, Thelonious Monk, and others. Public Enemy created noise with saxophone squeals, erratic drums, and countless scratches on Nation, a recording that has influenced numerous hip-hop artists and it stands today as a critically lauded and influential album. This paper investigates Public Enemy’s use of saxophone samples as a strategy of creating noise as representative of ideals contrary to conventional Western musical practices, and as a bridge to African-American musical practices and suppressed voices of the past.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".