Creative and Cultural Industry Entrepreneurship in the 21st Century: Challenges by and for Policymakers
Bibliographic record
Abstract
Abstract Our chapter focuses on the disconnect between economic and cultural policies and the needs of individual firms and creative industry professionals, all of which affect creative and cultural industry (CCI) entrepreneurship in the 21st century. After a review of selected policy trends and the overlooked role of creative industries in developing more sustainable liveable communities worldwide, we discuss recommendations by chapter authors in volumes 18A and 18B for useful policy actions, not only in and for their respective countries of study but also for other geographical contexts. Our particular focus is on how the CCIs have contributed to developing sustainable societies and meeting many targets of the Sustainable Development Goals. Thereafter, we provide an overview of the fifteen chapters distributed over five sections: ‘unusual and temporary places for CCI entrepreneurship’, ‘economic perspectives on CCI entrepreneurship’, ‘organising clustering of CCI entrepreneurs’, ‘cognitive aspects of doing CCI entrepreneurship’, and ‘social spaces and placemaking for CCI entrepreneurs’. Topics discussed include CCI entrepreneurship in rural areas (heritage entrepreneuring, book festivals), social work spaces, creativity and neuroentrepreneurship, strategic networking management for creatives, tensions from economic and artistic logics, collaboration challenges, street art and arts festivals. Countries considered include Estonia, Nigeria, Norway, South Africa, the United Kingdom, and Zimbabwe. We conclude the chapter with a selection of policy implications of chapters in both volumes 18A and 18B, and a research programme and manifesto for researchers to develop novel insights for policymakers, aimed at strengthening the important role of the CCIs in creating more liveable sustainable communities and economies.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".