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
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".