The Determinants of Outbound Tourism: A Revisit of Socioeconomic and Environmental Conditions
Why this work is in the frame
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Bibliographic record
Abstract
This article investigates the drivers of outbound tourism. The originality of our approach is that it integrates socioenvironmental aspects in the demand for international tourism. This study provides an empirical analysis for panel data of 82 economies from 2002 to 2016. Several estimates for panel data are applied. The results are robust and consistent. Beyond the classical economic drivers of tourism, socioeconomic factors, including urbanization, unemployment, vulnerable employment, and particularly aging population, are shown to play an important role in international tourism departures and international tourism expenditure. One of the notable findings is that environmental factors, including CO 2 emissions (positive) and forest area (negative), have a significant effect on international tourism. The results also show a stronger influence of economic, social, and environmental determinants of outbound tourism in higher income economies in the period after 2008.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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