Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it