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Record W3195006317 · doi:10.1177/13548166211029053

Hosting annual international sporting events and tourism: Formula 1, golf or tennis?

2021· article· en· W3195006317 on OpenAlex
Bala Ramasamy, Ho-Mou Wu, Matthew Yeung

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTourism Economics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAttractivenessTourismBusinessAdvertisingEconomic impact analysisMarketingPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Hosting sports events to attract international tourists is a common policy practised by many host governments. Hosting mega-sports events like the Olympics is 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 is 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 1998 to 2018, 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 Australia and the United Kingdom. ATP Tennis and PGA Golf have a significant impact on at least two countries. Policy-makers must consider carefully the sport that gives the best bang-for-the-buck.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.304
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it