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Record W746922241 · doi:10.5206/notabene.v7i1.6595

Sounds of Australia: Aboriginal Popular Music, Identity, and Place

2014· article· en· W746922241 on OpenAlexvenueno aff
James J. Wu

Bibliographic record

VenueNota bene · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSovereigntyPoliticsIdentity (music)Popular musicViewpointsColonialismAestheticsNegotiationSociologyPower (physics)Gender studiesMedia studiesPolitical scienceSocial scienceLawVisual artsArt

Abstract

fetched live from OpenAlex

During the late twentieth century, Australia started to recognize the rights of the Aboriginal people. Indigenous claims for self-determination revolved around struggles to maintain a distinct cultural identity in strategies to own and govern traditional lands within the wider political system. While these fundamental challenges pervaded indigenous affairs, contemporary popular music by Aboriginal artists became increasingly important as a means of mediating viewpoints and agendas of the Australian national consciousness. It provided an artistic platform for indigenous performers to express a concerted resistance to colonial influences and sovereignty. As such, this study aims to examine the meaning and significance of musical recordings that reflect Aboriginal identity and place in a popular culture. It adopts an ethnomusicological approach in which music is explored not only in terms of its content, but also in terms of its social, economic, and political contexts. This paper is organized into three case studies of different Aboriginal rock groups: Bleckbala Mujik, Warumpi Band, and Yothu Yindi. Through these studies, the prevalent use of Aboriginal popular music is discerned as an accessible and compelling mechanism to elicit public awareness about the contemporary indigenous struggles through negotiations of power and representations of place.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.405
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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