Best Practices in Implementing Tourism Satellite Account (TSA) and Its Ad Hoc Extensions: A Comparative Study in Selected Countries
Bibliographic record
Abstract
The Tourism Satellite Account (TSA) is an integrated statistical tool for measuring the economic importance of tourism in countries or regions. It allows measuring the relative weight of the tourism sector within a national and sub-national economy in a reliable and systematic approach as well as leading the process of developing a national system of tourism statistics which is considered as a challenging practice so far. This study aims to investigate the international experiences in implementing the TSA and in developing TSA's ad hoc extensions for the sake of suggesting guidelines to be adopted by countries as best practices in this area of tourism analysis. To achieve this study objectives, 6 countries' experiences in implementing TSA and its ad hoc extensions have been selected as the study's multiple cases; three developed countries (Canada- Australia- Denmark) and three developing countries (South Africa- Saudi Arabia - Egypt). The study concludes that, there is a clear disparity in the implementation level of TSA in the countries in question. Considering the study findings, a phased multi-stage process for research and development of the TSA and its ad hoc extensions is recommended.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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 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".