Formula 1, city and tourism: a research theme analyzed on the basis of a systematic literature review
Bibliographic record
Abstract
Purpose This paper aims to draw up the state of scientific knowledge in the field of Formula 1 with relation to tourism and urban studies. Design/methodology/approach This study is based on a systematic review of the scientific literature regarding this issue. Using targeted keywords and the analysis of various documentary databases, 8,075 references were identified and 40 documents were analyzed in an exhaustive manner. Findings This study presents a very nuanced portrait of the urban and tourism impacts of Formula 1 on the host territories. In many of the studies analyzed, a gap may be noted, sometimes flagrant, between the development goals of the promoters of these mega-events and local realities. This study also highlights the fact that Formula 1 has established itself as a sports events industry that can renew and enhance the brand image of certain cities. Originality/value Very few recent studies have exhaustively reviewed the scientific literature published in English and French with regard to the field of Formula 1 from a tourism and urban perspective. This study makes it possible to identify the main analytical findings and research perspectives resulting from this scientific work while discussing them using a theoretical framework related to the hypermodern character of different societies.
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.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".