The Changing Distribution of Global Tourism: Evidence from Gini Coefficients and Markov Matrixes
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
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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