Nascar and tourism : Analyses based on a scoping review of the literature
Bibliographic record
Abstract
Many countries, provinces and cities around the world use major sporting events as a catalyst for tourism development. The National Association for Stock Car Auto Racing (NASCAR), through its championships and racing events, has for many years, chosen to integrate itself into capitalist and neoliberal tourism and economic models. As a motorsport industry with strong historical, economic and media roots in American culture and certain values, NASCAR generates a myriad of tourism impacts on the territories hosting these races. This study, therefore, aims, through a scoping review of the scientific literature, to take stock of the scientific knowledge produced on NASCAR and its tourism impacts. This approach allowed the analysis of 28 scientific articles in depth and to draw several analytical conclusions. First of all, an observation was noted regarding a very strong involvement of sponsors and the media in this industry, which undeniably contributes to the creation of forms of sporting imagery around the teams and drivers). These sporting imaginaries undoubtedly colour the partisan cultures and even the fan communities that are created and evolve around and within this sporting ecosystem. The study of the tourist spin-offs of NASCAR has been studied in the scientific literature but appears to be rather limited or circumscribed, and must, therefore, be widely developed empirically.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.060 | 0.052 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".