Music Theory as Social Justice: Pedagogical Applications of Kendrick Lamar’s<i>To Pimp A Butterfly</i>
Bibliographic record
Abstract
Kendrick Lamar’s To Pimp A Butterfly offers core music theory instructors many opportunities: to engage with popular music in a curriculum traditionally focused on art music, to discuss theoretical topics not usually considered in the music theory core (including flow, groove, meter and rhythm), and to diversify the range of composer identities included in classroom repertoire. The album’s focus on African-American experiences of race and racism in the contemporary United States also allows instructors to integrate social justice topics with music-theoretical ones. This article discusses three possible models for such integration. In the “plug-and-play” model, examples from the album are embedded within lesson plans focused on traditional music theory topics. In the “concept” model, the undergraduate core curriculum is reorganized to focus on musical concepts rather than on analytical approaches to a particular repertoire, and musical examples from the album are used to explore analytical strategies for each concept. Finally, in the “social justice” model, the album is used as a springboard for classroom discussions and assignments about race, racism, poverty, and more, while still maintaining focus on analytical methods. For all three types, the author includes sample teaching materials including lesson plans, curricular design strategies, and teaching techniques. With this work, the author encourages instructors of all backgrounds, abilities, and institutional settings to consider ways of incorporating social justice into their own classrooms to change the world for the better.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".