Tourism Trends: Current Challenges for Tourism Destinations Management
Bibliographic record
Abstract
This introductory chapter presents and discusses some of the salient challenges and trends (i.e., climate change, overtourism, threats to diversity, security, technology, labour issues, and competitiveness) that the tourism sector is facing and that must be addressed by any government and manager who has the ambition to lead a responsible, sustainable, and competitive destination. The chapter proposes to consider tourism from a different and more holistic perspective: Tourism should not be viewed only as an economic engine that sells services, but as an activity that is part of a global natural and socio-cultural system which is impacted by tourism (both positively and negatively) and that should also contribute to improvement and sustainability. It discusses how smart destinations require innovative development, management, and marketing solutions to address those challenges and trends to meet sustainable development goals (SDGs). Finally, the chapter introduces and proposes the model of the Spanish Secretariat of State for Tourism, developed by SEGITTUR, for the Smart Tourism Management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".