The 2008 Olympic Games Leveraging a “Best Ever” Games to Benefit Beijing
Bibliographic record
Abstract
This paper undertakes a leverage analysis of mega-events in the context of the 2008 Beijing Olympic Games. A leverage analysis apparently different from an impact analysis; it focuses on how to maximize the potential positive impacts and minimize the negative impacts for an event that will be held in the future. To analyze the circumstance of the Beijing Olympics that are amendable to leveraging, the research methodology employed is empirical with method of semi-structured in-depth interviews with the government officials, tourism industry people and the local community members in Beijing. This research conducted a leveraging analysis from three perspectives: environmental, socio-cultural, and tourism/economic perspectives. From each perspective, four angles would be investigated: identifying the potential impacts of 2008 Olympics; determining the leveraging activities for the potential impacts; uncovering the opportunities for leveraging the potential impacts, and exploring the challenges in leveraging the potential impacts. Because there is not any existing theory on a leverage analysis in the context of Beijing Olympic Games, this research was conducted with the guidance of “grounded theory”. The research indicated that the government aims to take use of the opportunity of hosting the “Green Olympics” to reap the ambition of making Beijing into a “greener” city, but how long would the policies last when the games are over is essential for the effectiveness. Similarly, the socio-culture would be leveraged by implementing the “People’s Olympic” theme. Finally, the tourism in Beijing would be leveraged by a series of leveraging activities derived form the Olympic Games. The findings of this research will contribute to the event studies and the leveraging studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".