Increasing Trends of Tourist Flows from the European Countries to Georgia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it