An OECD Review of Statistical Initiatives Measuring Tourism at Subnational Level
Bibliographic record
Abstract
Regions and cities play a growing role in tourism development and policy design. The economy-wide effects of tourism are significant and vary a lot from one territory to another in terms of number of visitors, type of tourism, seasonality patterns, revenues and added value per visitor or jobs generated. This OECD review supports a better measurement of the various impacts of tourism at sub-national level by the sharing of good and innovative country statistical initiatives. The review will contribute to the development of reliable data and analysis at regional and local levels for business and policy decision making. The review presents statistical initiatives for Australia, Austria, Canada, Denmark, Finland, France, Ireland, New Zealand, Spain, Switzerland and the United Kingdom. The initiatives focus on a wide range of issues such as total economic impact of tourism; direct economic impacts of tourism; tourism-related employment; enterprise demographics; tourism spending/revenues and high yield visitors, data visualization; regional competitiveness; and sustainability.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.074 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".