Hosting Annual International Sporting Events and Tourism: Formula 1, Golf or Tennis (CEIBS Working Paper, No. 013/2020/ECO)
Bibliographic record
Abstract
Hosting sports events to attract international tourists is a common policy practised by many host governments. Hosting mega-sports events like the Olympics are said to leave a legacy that could impact the attractiveness of a country/city in the long term. However, the opportunity to host these mega-events are limited and expensive. This study considers the economic impact of hosting annual international sporting events, specifically the extent to which Formula-1, ATP Tennis, and PGA Golf can attract international tourists. Using monthly data from 1985 – 2018 and we show that the effect differs from one sport to another within a country, and the same sport across countries. Hosting the Formula-1 is most effective for Canada but has no significant impact in the UK. ATP tennis has a significant impact on all three countries but may not be the star event. Policy-makers must consider carefully the sport that gives the best bang-for-the-buck.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.021 |
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".