Bibliographic record
Abstract
Twenty years has passed since the TSA concept was first proposed in Canada. It was proposed to address real and perceived problems of credibility and comprehensiveness in tourism statistics. Since then, the WTO has successfully promoted and promulgated the TSA-RMF as the new international standard to be emulated in improving national tourism statistics. More recently, significant research and development progress has been made by leading countries, such as Canada, in applying the TSA, and developing TSA-related extensions and associated statistical instruments. This paper demonstrates the benefits of a close working relationship between tourism macroeconomic research and tourism market research. It demonstrates how, in addition to industry analysis, the TSA also enables the development of new market research tools, providing new knowledge and insights into the relationships between tourism industries and their markets. From these techniques and the industry and market data produced, it is then possible to further expand the technological base by developing additional tools for assessing both short and long term market and industry shocks and associated trends. The new derivative tools themselves provide new industry aggregates to measure overall tourism
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.738 | 0.634 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".