Impacts of the World Recession and Economic Crisis on Tourism: North America
Bibliographic record
Abstract
This article has been prepared as part of the International Academy for the Study of Tourism’s initiative to provide a better understanding of the impact that the economic crisis of 2008-2009 has had, is having, and will have on the well-being and performance of the global tourism industry. More specifically, it seeks to serve as an information “backgrounder” for the North American components of the study. As such, it provides a concise assessment of the current and evolving status of the tourism sector in each of the three countries in North America. Information on Canada was drawn from data provided by Statistics Canada, the country’s centralized government-funded statistical agency, as well as information bulletins issued by the Canadian Tourism Commission. Information on Mexico was drawn from a number of official reports prepared by several government agencies. Information on tourism in the United States was assembled primarily from available data from the U.S. Bureau of Economic Analysis from their U.S. Travel and Tourism Satellite Account system. In summary, the present backgrounder reveals that tourism in Canada and the United States has been, and is being, affected by the current economic crisis, and it appears likely that it will be further affected in the near future. In contrast, tourism in Mexico has been affected more directly and to a much greater extent by the swine flu pandemic, exchange rates, and weather conditions than by the economic crisis itself.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".