Regional Cultures, Economies, and Creativity : Innovating Through Place in Australia and Beyond
Bibliographic record
Abstract
Drawing on Australian and comparative case studies, this volume reconceptualises non-metropolitan creative economies through the ‘qualities of place’.This book examines the agricultural and gastronomic cultures surrounding ‘native’ foods, coastal sculpture festivals, universities and regional communities, wine in regional Australia and Canada, the creative systems of the Hunter Valley, musicians in ‘outback’ settings, Fab Labs as alternatives to clusters, cinema and the cultivation of ‘authentic’ landscapes, and tensions between the ‘representational’ and ‘non-representational’ in the cultural economies of the Blue Mountains. What emerges is a picture of rural and regional places as more than the ‘other’ of metropolitan creative cities. Place itself is shown to embody affordances, unique institutional structures and the invisible threads that ‘hold communities together’.If, in the wake of the publication of Florida’s Rise of the Creative Class, creative industries models tended to emphasize ‘big cities’ and the spatial-cum-cultural imaginaries of the ‘Global North’, recent research and policy discourses – especially, in the Australian context – have paid greater attention to ‘small cities’, rural and remote creativity. This collection will be of interest to scholars, students and practitioners in creative industries, urban and regional studies, sociology, geography and cultural planning.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".