Bibliographic record
Abstract
This chapter discusses cultural production in Australia, focusing on a case study of Indigenous popular music in remote parts of Australia. It is partly intended as a counterpoint to the thrust of much research on the geography of cultural industries, which focuses on agglomerations or clusters of activity in districts of major western cities. It is concerned with cultural production in some of the most remote parts of the world, and in circumstances of extreme socio-economic disadvantage. The chapter therefore seeks to examine the structure of cultural production in scattered, distant places that are vastly different from the conventional urban clusters, and explore how recent technological and political changes provide opportunities for more dispersed or decentralized activities. Cultural industries are usually most successful when production agglomerates in urban areas, particularly major metropolitan centers (Connell and Gibson, 2003), yet the creative activities (music making, writing, painting, etc.) upon which cultural industries rely take place across much wider distances and often dispersed contexts that are far from being hubs of capital and investment. The extent to which cultural activities in such locations may be transformed into export-earning industries is the focus of this chapter. It draws together earlier research projects on Indigenous production of popular music (Gibson, 1998; Connell, 1999; Dunbar-Hall and Gibson, 2004). These projects involved interviews with producers, managers, promoters and musicians, and analysis of production, employment and business location data. Insights drawn from this case study shed light on both the policy implications of cultural production by Indigenous groups in other countries (for example, in Canada and the United States), and the theoretical implications of creative workers being physically and economically distant from recognized centers of cultural production.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".