Modeling Nature-Based and Cultural Recreation Preferences in Mediterranean Regions as Opportunities for Smart Tourism and Diversification
Bibliographic record
Abstract
The tourism and recreational offer of Mediterranean destinations involves, essentially, the promotion of mass tourism, based on the appeal of the sun and beach, and the quality of its coastal assets. Alongside the impacts of climate change, poor tourism diversification represents a threat to the resilience of the territory. Thus, heterogenization of noncoastal tourism products presents an opportunity to strengthen regional resilience to present and future challenges, hence the need to study, comparatively, the complementary preferences of tourists and residents of these regions in order to unveil their willingness to diversify their recreational experience, not only in coastal spaces, but also—and especially—in interior territories with low urban density. Consequently, this strategic option may represent a way of strengthening resilience and sustainability through diversification. In this context, a survey was conducted among 400 beach tourists and 400 residents of a case study—namely, three municipalities of the Algarve region in southern Portugal—in order to analyze their degree of preference for activities besides the sun and beach, such as nature-based and cultural tourism activities, and to probe the enhancement potential of each tourism and recreational activity through the various landscape units considered by experts, stakeholders, and tour operators. The respective degree of preference and enhancement potential were indexed to the area of each landscape unit. Subsequently, respecting the existing recreational structure and constraints, a suitability map for territory enhancement and the implementation of smart tourism practices for each tourism activity and landscape unit is presented. Results show a significant preference for noncoastal outdoor recreational activities.
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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 | 0.001 | 0.004 |
| 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".