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Record W2999312388

Regional Cultures, Economies, and Creativity : Innovating Through Place in Australia and Beyond

2019· book· en· W2999312388 on OpenAlexaboutno aff
Ariella Van Luyn, Eduardo de la Fuente

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityEconomic geographyEconomyBusinessSociologyGeographyPolitical scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

"Drawing on Australian and comparative case studies, this volume reconceptualizes 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"--

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.126
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.087
GPT teacher head0.328
Teacher spread0.241 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2019
Admission routes1
Has abstractyes

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