Winter Olympic Games, cities, and tourism: a systematic literature review in this domain
Bibliographic record
Abstract
The purpose of this study is to present the current state of scientific knowledge on the Winter Olympic Games (2000 to present-day) and their urban and tourism-related impacts. To achieve this, a scoping review was performed using established methodology. Of the 1694 English and French peer-reviewed sources identified from 14 different databases, 47 met the specific inclusion criteria and were retained for analysis. Findings were divided into three sections according to our objectives: (1) methodological profiles of the selected articles; (2) urban impacts; (3) tourism-related impacts. First, the reviewed sources – mainly qualitative – generally showed that mega-events such as the Winter Olympic Games are a catalyst for the urban renewal of host cities. However, these urban transformations must be part of a global scenario to ensure long-term viability. Although research shows that the Games represent an opportunity for the development of the tourism industry, the scoping review showed mixed results in terms of tourist flows and the enhancement of the city’s image. The concluding remarks identify the limitations of this study and offer opportunities and areas of research regarding the next Winter Games.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".