Competitiveness, economic legacy and tourism impacts: World Cup
Bibliographic record
Abstract
Tourism legacy impacts are considered one of the most important reasons for countries host a mega event. They generate short, medium and, long-term legacies in the destination image, as well as, at economic and sociocultural level. The paper objective is to show the short-term evolution of economic legacies indicators of sports mega-events, two years before and two years after each event. It was verified through an exploratory analysis of tourism employment level, total investment, and Tourism GDP of the countries that hosted the last three FIFA World Cups. Also, it analyzes the country’s competitive strengths at the year that the mega-event happened based on the Tourism & Travel Competitiveness Index pillars (TTCI). Thus, it performed an explanatory and descriptive analysis of secondary data. Literature review points out the infrastructure as the central pillar to receive a sports mega-event. However, the results did not indicate this focus in the last countries chosen. Also, the economic legacies had increased after the sports mega-event, but not in all hosts analyzed. Therefore, the assumption of positive effects generated by sports mega-events is not consolidated. Further research is recommended to establish indicators and approaches the impact on the host country by a mega-event from the legacy perspective.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".