Circular Economy of Cultural Heritage—Possibility to Create a New Tourism Product through Adaptive Reuse
Bibliographic record
Abstract
Cultural heritage is a particularly significant resource in creating tourism. When a local community recognizes its cultural heritage (small historic towns, buildings, castles, and forts), it is possible to create new value to meet the needs of tourists, using the principles of a circular economy. Adapting, reusing and restoring heritage sites can contribute to the revitalization of the local economy by creating jobs (increased employment), increased spending, economic development, etc. Adaptive reuse, as one of the principles of a circular economy, represents how the circular economy can pave the way to create new tourism products. The three basic principles of sustainable waste management are reduce, reuse, and recycle (3R). This paper tackles the reuse principle by analyzing case studies involving the application of a circular economy to cultural heritage in the Kvarner tourism destination (Croatia) in the context of reusing resources to create a sustainable destination. The goal is to determine to what extent the reuse of heritage sites makes them useful for the local community, and for tourists to stay in the destination. The research showed positive examples in the Kvarner tourism destination, primarily of a cultural tourism nature and that were achieved in the last ten years; however, the conclusion is that this is still insufficient. By aggregating knowledge and research results, the paper emphasizes the importance of applying the concept of the circular economy to cultural heritage in tourism destinations, with special emphasis on the role of all stakeholders in creating sustainable heritage tourism (local self-government, destination management, local population, and entrepreneurship).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".