Bibliographic record
Abstract
Gender and issues in ethnicity, race and indigeneity are integral to music cultures, but seem surprisingly seldom addressed critically in ethnomusicology (Chuse 2003).Independent of genre and location, ethnography focusing on structures of power and questions of identity and difference can focus on British punk as well as Sámi rap.In a new monograph, Åse Ottosson addresses aboriginal music culture and masculinity in central Australia, focusing on aboriginal rock, country and reggae by musicians originating from the desert region of the Northern Territory, Australia.Research conducted in the central Australian desert that addresses music culture and gender, as well as indigenous and First Nation identity is scarce.In other locations research questions on gender, music and indigenous identity have been addressed as interlinked: for example, by wellknown scholars like Beverley Diamond (2000) and Tina K. Ramnarine (2013) exploring gender, femininity and indigeneity in First Nations of Canada and Sámi musics.One, of many, interesting contributions in Making Men and Music in Central Australia is the discussion of the diversity within Aboriginal music culture, in particular the different musical practices that are evident in various regions of Australia.The music and musicians, their studio work, touring, performances and backstage practices are mapped within Ottosson's detailed ethnography, and their way of making music is contrasted with Aboriginal music cultures from the top
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".