Increasing Trends of Tourist Flows from the European Countries to Georgia
Bibliographic record
Abstract
Tourism is developing in Georgia and it is the fact that the exemption of visa limitations has had an important impact on the growth of tourist flows. It may be assumed that significantly increased flows of EU citizens to Georgia in recent years are an immediate result of the liberal visa policy. Research methodology: methods of statistical observation, grouping and analysis were used in the research process. The number of total visitors to the country and that from the European Union increases annually. As the data of 2018 suggest, the visits to Georgia for 72.9% of the international visitors were recurring, while 27.1% of the visitors were on their first visit in Georgia. Visits from the EU are most common in the III quarter of the year, i.e. in summer. EU visitors are mostly from Poland, Germany, UK, France, Lithuania and other countries. Most visitors are of the 26-35 age group. The most visited place is Tbilisi. The visits from the EU show a generally increasing trend, with the greatest increase fixed in 2018 as compared to the previous year; men dominate among the international visitors. The EU countries show a similar regularity; as to the age categories, 31-50 age group dominates among the international visitors and 26-65 age group dominates among the EU visitors; a leading country with the largest number of visits from the EU is Poland; the degree of satisfaction is high, with only 1.7% of the international inbound visitors being discontent.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".