The use of intangible heritage and creative industries as a tourism asset in the UNESCO creative cities network
Bibliographic record
Abstract
The creative economy has been recognized as key in urban development and planning, which the UNESCO Creative Cities Network (UCCN) consolidates. While benefiting from the label, the tourism sector also plays a fundamental role in the creative strategy. This paper explores how intangible heritage and creative industries can work as a tourism asset for creative cities and thus participate in their development. An NVivo thematic content analysis of all the tourism-related actions listed in the UCCN reports was performed to identify what types of cultural tourism products and actions are linked to the creative cities and to understand how they relate to their UNESCO creative fields to detect gaps and potentials. Tourism activity represents 17% of the total actions listed in the creative cities' reports, mostly concentrated in the Crafts & Folk Art field. The empirical results highlight tendencies that can be applied and adapted to future destinations with intangible assets on their territory and that want to work with the creative industries. Thus, this paper unveils an underexplored potential of synergies between two important economic and creative activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".