Can Entrepreneurial Ecosystems Optimize the Impact of Mega-sport Events? Evidence from the 2014 Fifa World Cup And 2016 Summer Olympic Games in Brazil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it