Classification of Countries of Destination by Gross and Relative Values of International (Inbound) Tourism and its Factors
Bibliographic record
Abstract
The present work is aimed at the analysis of gross and relative values of inbound tourism by countries of destination for the purpose of their classification. As a result of confronting total and specific (per 1 km of conventional radius of the country’s territory) numbers of international tourist arrivals with the median values for 100 countries of the world as of 2016, countries of destination were divided into four classes. Small countries of intensive inbound tourism are predominantly represented by tropical islands of the Caribbean Basin and Indian Ocean, as well as by the Mediterranean region. Over half of big countries of intensive inbound tourism are located in Europe and the Mediterranean destinations were the most often visited ones. Big countries of extensive inbound tourism show significant volume of inbound tourism in the first place due to their significant territories. Among these, there were Scandinavian destinations of Europe, Canada and Russia. The low intensity of their inbound tourism is explained by the unfavourable climate for human thermal-physiological sensations. Small countries of not-intensive inbound tourism had considerably less volume and intensity of tourism arrivals due to their small territories, unfavourable geographical conditions, but, what is most essential, also due to the poverty. In addition, cost indicator, that is receipts from inbound tourism per one arrival, was taken into account in the analysis. The factors that have influence over it were also disclosed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".