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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".