Can Entrepreneurial Ecosystems Optimize the Impact of Mega-sport Events? Evidence from the 2014 Fifa World Cup And 2016 Summer Olympic Games in Brazil
Bibliographic record
Abstract
Entrepreneurial ecosystems (EE) have emerged as a viable method for stimulating traditional measures of economic development. In parallel, the effect of mega-sport events (MSEs) on economic development has been documented as perfunctory at best, despite the best efforts of municipalities and sport governing bodies. A natural extension of these lines of work asks whether EEs can play a role in enhancing the impact of MSEs within a host region. Therefore, this study sought to assess how a nation's EEs affected innovation outcomes during the hosting of two back-to-back MSEs. Using the 2014 FIFA World Cup and 2016 Summer Olympic Games as the context, a sample of 2,951 venture capital transactions made to startups in South America were analyzed using a generalized policy analysis framework. The findings suggest that a well-established EE may have helped enhance venture capital availability during the time of the MSEs, but that a less robust EE did not generate any positive effects. These findings bolster the economic work documenting that adequate resources and infrastructure are prerequisites for host regions to realize benefits from MSEs, not an outcome to leverage MSEs toward.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".