After the Crowds: Redemption Frameworks for Overbuilt Olympic Sports Venues
Bibliographic record
Abstract
Many studies have documented the common problem of venue underutilization after mega events such as the Olympic Games and the FIFA World Cup, which imposes a heavy financial burden on the host cities. This paper aims to provide practical and empirical suggestions for improving venue underutilization through research and analysis of dismantled and existing sports facilities. Under the sharing and circular economy frameworks, we comparatively analyze the venue sustainability of different mega sports venues through site selection, construction, and after-event operation phases. In the site selection phase, we suggest choosing a location near the city, cooperating with universities, and building on existing infrastructure. In the construction phase, we recommend refurbishing or reusing existing sports stadiums to optimize space utilization rates, enhancing the versatility of venues, and using reusable materials and renewable energy sources. Suggestions for after-event operations include sharing sports stadiums for multiple purposes and improving resource reallocation. Our paper improves the venue utilization of mega sporting events from circular and sharing perspectives.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".