Regional Political Risks and Sustainable Tourism Development Tendencies: A Case Study
Bibliographic record
Abstract
The tourism industry is extremely susceptible to political and social unrest.Countries aiming to develop their tourism sector understand this and strive to maintain peace and economic stability.However, for developing nations such as the Georgia, with its strategically attractive location, ensuring socio-economic stability presents a significant challenge.This difficulty is being solved by creating an adaptive environment that fosters both tourism growth and regional economic well-being.The main aim of the article is to examine Georgia as a case study to explore how political issues can influence tourism industry sustainable development.While political instability often hinders tourism, Georgia's case deviates from the norm, potentially offering new approaches for tourism development in such circumstances.This study employs a Vector Autoregression (VAR) model to analyse the impact of various factors, such as -the correlation between the Index of Global Real Economic Activity and tourism revenues, GPRH (Geopolitical Risk Index), and geopolitical events on Georgia's tourism industry from 2006 to 2022.The analysis will focus on the period before and after the war to assess its influence.By analysing the relationships between these variables, the study aims to understand how global economic conditions, geopolitical instability, and the war specifically, have influenced the evolution and economic effects on Georgia's tourism industry.This study's key finding reveals a positive correlation between tourism revenues and geopolitical risks in Georgia, even considering the war.This finding suggests a more nuanced relationship between these factors than previously assumed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".