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Record W3118011110 · doi:10.3390/su13010170

A Case of Leveraging a Mega-Sport Event for a Sport Participation and Sport Tourism Legacy: A Prospective Longitudinal Case Study of Whistler Adaptive Sports

2020· article· en· W3118011110 on OpenAlexaboutno aff
Tracey J. Dickson, Simon Darcy, Chelsey P. Walker

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPublic relationsTourismSport managementContext (archaeology)MarketingPolitical scienceBusinessAdvertisingGeography

Abstract

fetched live from OpenAlex

Sport participation legacies are often offered as reasons to host mega-sport events, yet there is little evidence to demonstrate the claim’s legitimacy, thus we examine “What did Whistler Sports do to leverage the Vancouver 2010 Olympic and Paralympic Winter Games to facilitate a sport tourism legacy?”. Through a prospective longitudinal case study of WAS and application of the temporal extension of the socioecological framework, multiple data sources were analyzed from over a decade beginning before the event until 2019. The findings reveal the situated and embedded nature of mega-sport event legacies i.e., context. These depend upon a network of facilitators such as local, provincial, and federal policies; pre-event and post-event vision and strategies from local communities and sport organizations; the development of a pool of willing and flexible volunteers. Together these were strategically leveraged to overcome sport participation and sport tourism barriers for people with disabilities. The sport, tourism, and sport tourism experience reflected Whistler’s natural and infrastructure advantage and the needs and desires of locals and visitors with access needs that could not have occurred without the capital injection of the Vancouver 2010 Olympic and Paralympic Games. Leveraging the mega-sport event opportunities required leadership and a strategic vision for repositioning to a year-round program. This strategic change also opened new sport and sport tourism opportunities for current participants but importantly brought new participants and their friendship groups to Whistler over the post-event decade for year-round sustainable adaptive sport opportunities.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.058
GPT teacher head0.365
Teacher spread0.307 · 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 designObservational
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

Citations34
Published2020
Admission routes1
Has abstractyes

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