UNWTO World Tourism Barometer and Statistical Annex, May 2019
Bibliographic record
Abstract
International arrivals grew 4% in the first quarter of 2019 International tourist arrivals (overnight visitors) grew 4% in January-March 2019 compared to the same period last year, below the 6% average growth of the past two years. Growth was led by the Middle East (+8%) and Asia and the Pacific (+6%). Europe and Africa (both +4%) and the Americas (+3%) also recorded an increase in arrivals in this first quarter of 2019. Confidence in global tourism performance has started to pick up again after slowing down at the end of 2018, according to the latest UNWTO Confidence Index survey. The Panel’s outlook for the current May-August period is more optimistic than in the past three periods and more than half of respondents are expecting a better performance in the coming four months. Total exports from international tourism reach USD 1.7 trillion in 2018 Total export earnings from international tourism reached USD 1.7 trillion in 2018, or almost USD 5 billion a day on average. International tourism (travel and passenger transport) accounts for 29% of the world’s services exports and 7% of overall exports of goods and services. For the seventh year in a row, growth in tourism exports (+4%) was higher than growth in merchandise exports (+3%) in 2018.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.036 |
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".