Reputational capital and olympic events: a case study of whistler live!
Bibliographic record
Abstract
Mega events such as the Vancouver 2010 Winter Olympic and Paralympic Games present unique opportunities to increase the economic and social capital required by destinations to be competitive on the global tourism stage. Engaging Games and community stakeholders in the networks needed to organize and deliver such events is central to creating sustained and positive legacies. Network building and maintenance can occur at a variety of levels and scales. Effective and sustained networks depend on and are shaped by the social and reputational capital created through the process of managing various dimensions of the event. One of the more recent Games’ dimensions used as a vehicle for creating social capital is the Cultural Olympiad. This dissertation creates and tests the utility of a conceptual model in identifying how event organizers strategically select stakeholders and nurture network relations to build the reputational capital needed for sustained competitiveness. It builds this model based on premises and principles emerging from literature related to corporate social responsibility, social capital development, reputational capital creation, Olympic mega-event legacies, tourism destination branding and community based sustainability planning. The study tests the model’s usefulness through a case study of the stakeholders, networks, and outcomes created in the development and delivery of Whistler’s portion of the 2010 Winter Games Cultural Olympiad – ‘Whistler Live!’. It explores the ways in which Whistler engaged its stakeholders and partners so as not only to meet its immediate Olympic goals, but also to contribute the longer term reputation and sustainability of the resort community.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".