Strategic Vectors of Coastal Tourism Development as a Blue Economy Component in the International Dimension
Bibliographic record
Abstract
The blue economy covers various scientific areas and types of socio-economic activity that are related to each other: fisheries, shipping, tourism (beach and cruise), transportation, logistics, alternative energy, ecology, water management, climate change and biosphere conservation. It was found that coastal tourism occupies an important place in the system of the Blue Economy. According to the set of indicators: The Travel and Tourism Competitiveness Index, International tourist arrivals, thousands, Travel and Tourism industry GDP, and Employment in the tourism and travel sphere, a cluster analysis has been created for leading countries. As a result of the analysis, 9 clusters have been formed, for each of which strategic vectors of development were determined. The coastline of all countries in the world is 1162.3 million km. The longest coastline belongs to Canada – 202080 km. Relative indicators per 1 km of coastline as international tourist arrivals, international tourism inbound receipts, Tourism and Travel industry GDP have been considered. It has been substantiated that the Spanish coastal tourism industry deserves special attention. There are 17 coastal areas and more than 2,000 beaches in Spain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".