The Changing Distribution of Global Tourism: Evidence from Gini Coefficients and Markov Matrixes
Bibliographic record
Abstract
This article examines the global distribution of tourism arrivals over 1995–2008 to determine whether there is a pattern of concentration or dispersal of tourist arrivals at a global scale, and then predicts the possible future distribution of global tourists arrival based on changes in those years. The study employs Gini coefficients and a Markov matrix to international arrival data in 153 countries for the period between 1995 and 2008. The Gini coefficient is used to measure the dispersion of total inter- national tourist arrivals (ITA) in each country. Results show that the Gini coefficient has decreased over time (i.e., the distribution is gradually dispersed but the overall pattern remains unchanged). Using the same data, Markov matrix is used to predict the future distribution based on changes over the 14-year period. These results suggest future dispersion of international tourist arrivals would be somewhat different than it is today but the overall dominance of the leading countries (i.e., those with high arrival numbers) will continue. The implication is that the leading countries must develop strate- gies to continue to remain competitive, as other less visited countries make stronger efforts to pro- mote tourism to counterbalance the current imbalance in international arrivals.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".