International Tourism Development in the Context of Increasing Globalization Risks: On the Example of Ukraine’s Integration into the Global Tourism Industry
Bibliographic record
Abstract
Today, international tourism is one of the most affected sectors of the economy due to the global COVID-19 pandemic. The main purpose of this article is to analyze current trends and identify prospects for the international tourism development in the context of increasing globalization risks in the world, using the example of Ukraine’s integration into the global tourism industry, as Ukraine is located in the centre of Europe and belongs to a number of countries with developing economies, and has the potential to expand its tourism industry, which may be of interest to the international scientific community in terms of overcoming the bifurcation point of its economic development. Analyzing the tourism industry, as one of the most progressive sectors of the world economy, we used general scientific and special research methods (abstract-logical, statistical, systemic analysis and synthesis, abstract-theoretical, and correlation-regression analysis). The paper analyzes major indexes of international tourism development in the modern globalized world and details the risks emerging during the global COVID-19 pandemic. It examines the global dynamics of tourism flows, where France, Spain, and the USA are unquestionable leaders. The study considers foreign exchange earnings of international tourism and the industry contribution to the gross domestic product of countries being an essential component of national budgets. Based on the study conducted, there were developed reliable forecast models for the tourism industry development in the countries under research. These models will provide an opportunity to generate reliable forecasts, which will allow timely identification of potential threats and making effective decisions to address them. At the same time, the issues of managing information support of economic entities in the field of international tourism need to be further developed in order to reduce risks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".