Sustainability Determinants of Cultural and Creative Industries in Peripheral Areas
Bibliographic record
Abstract
Cultural and Creative Industries (CCIs) are increasingly recognized as part of the global economy and of growing importance for sustainable local development. However, the exploitation of their full potential depends on several issues concerning their entrepreneurial dimension and the context where they operate. The paper deals with these issues having the scope to investigate the main determinants of CCIs’ sustainability in peripheral areas, to understand what kind of policy could better support the survival of CCIs and development in these areas, according to an end-user perspective. The research is part of an Interreg Greece-Italy project carried out from mid-2018 until the end of 2020 with specific reference to CCIs in Apulia (IT) and Western Greece (EL). A two-step mixed methodology has been used to figure out regional specializations and the specific aspects of the entrepreneurial structure and business sustainability in the cultural and creative sector (CCs). In the end, the paper shows and discusses the main determinants considered crucial for CCI sustainability, suggesting guidelines for local authorities supporting their economic development.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".