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Record W2765833486 · doi:10.1080/14775085.2017.1389298

Winter Olympic Games, cities, and tourism: a systematic literature review in this domain

2017· article· en· W2765833486 on OpenAlexaff
Marilyne Gaudette, Romain Roult, Sylvain Lefèbvre

Bibliographic record

VenueJournal of Sport & Tourism · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec à Montréal
Fundersnot available
KeywordsTourismInclusion (mineral)Regional scienceGeographyEnvironmental planningUrban tourismDomain (mathematical analysis)Qualitative analysisTourism geographyMarketingPolitical scienceQualitative researchBusinessSociologySocial science

Abstract

fetched live from OpenAlex

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 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)0.003
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.300
Teacher spread0.283 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2017
Admission routes1
Has abstractyes

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