Bibliographic record
Abstract
In this very special interview for Critical Studies in Improvisation, two esteemed researchers come together to discuss hip-hop, improvisation, and Black expressive culture. George Lipsitz is Professor of Black Studies and Sociology at the University of California, Santa Barbara. His publications include The Fierce Urgency of Now: Improvisation, Rights, and the Ethics of Cocreation (co-authored with Daniel Fischlin and Ajay Heble), How Racism Takes Place, and Midnight at the Barrelhouse: The Johnny Otis Story. He serves as President of the Board of Directors of the African American Policy Forum and chairs the Advisory Board of the University of California, Santa Barbara Center for Black Studies Research which is an institutional partner of the International Institute for Critical Studies in Improvisation. Tricia Rose is an internationally respected scholar of post civil rights era black U.S. culture, popular music, social issues, gender and sexuality. She is most well known for her groundbreaking book on the emergence of hip hop culture, Black Noise: Rap Music and Black Culture in Contemporary America, considered a foundational text for the study of hip hop, one that has defined what is now an entire field of study. In 2003 Rose published a rare oral narrative history of black women's sexual life stories, Longing To Tell: Black Women Talk About Sexuality and Intimacy. In 2008, Professor Rose returned to hip hop with The Hip Hop Wars: What We Talk About When We Talk About Hip Hop-And Why It Matters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".