Bibliographic record
Abstract
Abstract This book explains the music, demographics, cultural issues, and industry surrounding Brazilian jazz composed and performed in New York City by professional musicians between 2000 and 2020. An ethnomusicological study based on original fieldwork and fifty-plus interviews, the book describes how musicians combine nationally associated genres and navigate the music industry while they expand their self-identities transnationally. Chapter 1 compares an original dataset of 173 musicians, their instruments, and social categories (nationality, race, and gender) to published data about Brazilian immigrants in the United States and jazz musicians in New York. It argues that systemic racism, sexism, and classism have caused imbalanced demographics among the musicians: approximately 70 percent are male and 70 percent are white; half are Brazilians, a quarter are US-born Americans, and the rest immigrated from Japan, Israel, Canada, Europe, and elsewhere in South America. Chapter 2 applies a framework of transnational polymusicalities—combining transnationalism with bimusicality from ethnomusicology—to interpret musicians’ affinities and identifications with Brazil and the United States, acquired through prolonged engagement with music. Chapter 3 considers the popularity of bossa nova among Brazilian-jazz fusions, as well as its relationship with jazz and, compared to Carnival samba, its alternative image of femininity and romance. Chapter 4 explains the fusion of genres in samba jazz, an improvised, up-tempo, instrumental style related to bossa nova. Chapter 5 outlines changing business practices by musicians, show presenters, and record producers from the 1990s into the Covid-19 pandemic that started in 2020.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.801 | 0.556 |
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".