Research on Organizing Strategies for the Olympic Games Based on the TOPSIS Method
Bibliographic record
Abstract
Fewer and fewer countries have bid to host the Olympic Games in recent years because of the negative short- and long-term impacts that bidding to host the Games has had on the country. A sustainable and healthy Olympic Games will have a huge positive impact on the country, so it is quite meaningful to assess the ability of an Olympic Games to be sustainable. In order to assess the success of the Olympic Games, we designed the Olympic Sustainability Index (OSI) as a primary indicator. Then, using McKinsey Logic Tree Analysis, we constructed a three-level evaluation index system, and we designed three secondary indicators and 10 tertiary indicators. The entropy weight method was used to determine the weights between the three-level indicators, and CRITIC weighting method was used to determine the weights between the second-level indicators, and finally an OSI evaluation model was built. After that, we collected the data of tertiary indicators from 1992 to 2020 Winter Olympics and Summer Olympics, and based on the above evaluation model, we got the OSI of each Olympic Games, among which the Vancouver Winter Olympics and Beijing Summer Olympics had the highest index. Since the OSI of the Olympic Games fluctuates greatly, in order to attenuate it, we propose the strategy of "fixed host city", i.e., selecting Vancouver and Beijing as the host cities of the Winter Olympic Games and the Summer Olympic Games, respectively.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".