UNWTO World Tourism Barometer and Statistical Annex, November 2018
Bibliographic record
Abstract
Continued healthy growth in international tourism in the first nine months of 2018 International tourist arrivals (overnight visitors) grew 5% in the first nine months of 2018 over the same period last year, reflecting a continued strong economic situation globally. The 5% growth consolidated the results of 2017 (+7%), yet growth somewhat slowed down through the third quarter compared to the strong first months of 2018. The same trend is seen in terms of global economic growth softening. All world regions enjoyed robust growth in the first nine months of this year, fuelled by strong demand from major source markets. Asia and the Pacific led growth in January-September 2018, with arrivals increasing 7%. Europe and the Middle East also recorded sound results with 6% growth, while Africa saw a 5% increase. The Americas grew more modestly at 3% this nine-month period. Preliminary data on international tourism receipts confirm the positive trend seen in international tourist arrivals, with particularly strong results in Asian and European destinations. Among the top 20 world spenders on outbound tourism, France, the United Kingdom, Australia, the Russian Federation, Spain, and India all posted double-digit growth in expenditure.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| 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) | 0.066 | 0.050 |
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".