Sustainable tourism development and the world heritage status of the Wadden Sea: The case of Terschelling
Bibliographic record
Abstract
National governments and regions make great efforts to obtain international recognition for their natural heritage, for instance through UNESCO’s World Heritage Sites list. Since June 2009, the Dutch Wadden Sea has been on the World Heritage List. Our study investigates to what extent the World Heritage status of the Wadden Sea matters to tourists visiting the Wadden island of Terschelling, in the off season. Results showed that for most respondents (93.5%) the World Heritage Status was not a reason to visit Terschelling, while almost a quarter (23.7%) did not know about the World Heritage Status. However, the Wadden Sea was regarded as one of the most attractive elements of the area, together with the North Sea beach, dunes and forests of the Wadden island of Terschelling. For 58% of the respondents, the Wadden Sea was the reason to visit. To conclude, the Wadden Sea is highly appreciated, but for domestic tourists, the designation as World Heritage Site is not a reason for visiting. First-time or international tourists, who are more likely to be attracted by the World Heritage status, were not found during the off season. More efforts could be made to promote the Wadden Sea area as an attractive natural area among these groups of tourists, in order to gain more support for nature protection and preservation of this unique natural heritage region.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".