Bibliographic record
Abstract
Introduction Many cities aggressively compete to host sport events for the anticipated economic benefits from sport tourists, infrastructure development, foreign investment, and international prestige (Gratton, Shilbi, and Coleman, 2005). Sport mega-events, i.e., Olympic Games, FIFA World Cup, UEFA EURO Cup, Commonwealth Games, as well as second-tier and relatively smaller events, can have significant social, economic, and environmental impacts on host communities. Further, the brand and image of a destination may in some cases become inextricably linked to the sport event i.e. Innsbruck, Austria (Winter Olympics); Augusta, Georgia (Masters Golf Championship); Barcelona, Spain (Summer Olympic Games). Research on sport tourism events has evolved from Ritchie and Aitken's longitudinal research on the Calgary Winter Olympic Games (1984) and the comprehensive assessment of the Adelaide Grand Prix (Shaw et al., 1989). The articles examines the progression and current state of sport event tourism research with respect to stakeholder and social exchange theories, universal access (Darcy, 2001), impact assessments (environmental, social/cultural, and economic) (Baade & Matheson, 2004; Burns & Mules, 1989), consumer behaviours (Scott & Turco, 2007); image, legacy (Preuss, 2007; eitchie, 2000), and sustainability. Emphasis is placed not only on what research has been performed and how, but why. In addition, key issues in sport event tourism research are discussed and new areas for research are explored. First, I will define and operationalise some key terms and concepts central to sport event tourism. Sport events come in various formats, sizes, and durations. There are tournaments, races, series and circuits, friendly exhibitions, and highly competitive and combative bouts. The constant is sport: Physical competition, governed by rules and norms, with a declared winner or winners. Prominent events are often sized as mega, hallmark, or large-scale. Hallmark events are major one-time or recurring events of such magnitude that they place significant impacts on the host city or region. In sport, there are really two mega-events, the FIFA World Cup and Summer Olympic Games. The Commonwealth Games, Cricket World Cup, Super Bowl, are large hallmark sport events. Some events extend for weeks i.e. Rugby World Cup, and others, Kentucky Derby, are over in two minutes, though the post-race party lingers on. Sport events in small communities may produce relatively similar impacts as larger events in larger cities or countries. Sport tourists There are two primary types of sport event tourists: Participants and spectators. Participants travel to event destinations to play sport. They may be elite professionals or rank amateurs. Participatory sport tourists may include runners in the Berlin Marathon or mountain bikers in Moab, leisure skiers in Innsbruck or young Finnish footballers playing a series of friendly matches in Spain. The qualifier for participatory sport tourists is that they are visitors to the host destination and they are actively engaged in sport. Sport tourist spectators are visitors who watch others participate in sport. They may travel to witness their favorite team play away matches as is the case with Manchester United's many fans, accompany loved ones at sport contests, or attend the Olympic Games or World Cup. Consider the Little League World Series in tiny Williamsport, Pennsylvania. Held annually in August, the Little League World Series is the pinnacle of athletic achievement for boys up to 13 years of age. Teams from around the world compete in round robin and elimination play, attracting over 500,000 spectators and live television coverage worldwide on ESPN. The athletes, coaches, and officials are also sport tourists to Williamsport for the ten days of the LLWS. Stakeholder theory contends that there are other key parties involved in a sporting event besides athletes, organizers and spectators, including governmental bodies, media, sponsors, prospective fans, and the public at large. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".