Disruptive Dialogics: Improvised Dissonance in Thelonious Monk and Wu-Tang Clan’s 36 Chambers
Bibliographic record
Abstract
My performative paper makes the suggestion that the act of incorporating Jazz samples into Hip Hop music is a type of disruptive performance that interprets a standard musical form to create new styles, producing hybrid genres of music, such as jazz-rap. I examine the sample as a cultural tactic that enacts what Hip Hop scholar Tricia Rose terms as “flow, layering and ruptures in line.” “Layering” is comparable to what we might term in literary studies as intertextuality. DJs layer sounds literally one on top of the other, creating dialogues among samples, sounds, and words; the layers function as dialogue, commentary, percussive rhythms and counterpoint. Julia Kristeva argues that “any text is constructed as a mosaic of quotations; any text is the absorption and transformation of another. The notion of intertextuality replaces intersubjectivity, and poetic language is read as at least double.” In Hip Hop, visual, physical, musical, and lyrical lines are set in motion and broken abruptly with sharp angular breaks, yet they sustain motion and energy through fluidity and flow. The song “Shame on a Nigga” (from 36 Chambers) samples “Black and Tan Fantasy” as performed by Thelonious Monk. Using sampling as a tactical style that denotes improvisation as a disruption (or break) from classical European tradition to posit African American priorities, many of the songs on 36 Chambers participate in polyphonic layering and center around three central concepts outlined in Tricia Rose’s Black Noise: flow, layering, and ruptures in line. Further, the rupturing effect of incorporating a jazz sample into a Hip Hop production compels us to think about how disruption can function as a model for critical practice that relates to the notion of a globally improvised community.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".