Accelerators as start-up infrastructure for entrepreneurial clusters
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
Infrastructure is commonly conceptualized as a set of facilities that play a critical role in facilitating activities by individuals and organizations. Conventionally, infrastructure is tightly linked to publicly funded projects that facilitate access to key resources and enable diverse activities. Within entrepreneurial clusters research, infrastructure includes universities, research institutions and telecommunication technologies that facilitate entrepreneurial activities. These capital-intensive investments seek to facilitate start-ups emergence by aiding access to markets and development of ideas. Accelerators facilitate the same activities and have only recently been conceptualized as start-up infrastructure. This study builds upon this research stream by elaborating on how accelerators can play this meaningful role at the cluster level. Specifically, and by relying on the analysis of empirical evidence from three distinct studies, we uncover how accelerators provide tangible and intangible dimensions of start-up infrastructure to form a positively reinforcing cycle of entrepreneurial activities. Additionally, our findings allow us to push further the idea that start-up infrastructure development can be an endogenous process involving multiple actors within the cluster. Our empirical findings and the theoretical insights derived from them have meaningful implications for the aforementioned literature, as well as start-up practitioners and policymakers linked to the funding of entrepreneurial clusters.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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 it