Examining the Mega‐Event Space–Perception Nexus: An Advanced Epicenter Effect Perspective
Bibliographic record
Abstract
Previous research emphasizes that residents living within an event’s epicenter (i.e., host city) will exhibit the greatest positive and negative event legacy perceptions. However, given that mega‐events often include multiple event spaces to operationalize hosting (e.g., satellite cities), a single epicenter perspective is challenged. We examined residents’ social legacy perceptions of a mega‐event with multiple event sites to test an epicenter effect within this event ecosystem. Data were collected via surveys from 1,901 residents living within four event spaces: Host City , Satellite , Provincial , and National . Statistical analyses revealed event space significantly influenced residents’ social legacy perceptions but not linearly as previously theorized. Rather, Satellite residents perceived the highest positive legacies, not Host City residents. This evidence advances epicenter effect theorizing by highlighting how various event spaces can amplify or diminish residents’ perceptions. Event managers should leverage multiple event spaces to maximize positive legacy perceptions while minimizing negative legacy perceptions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".