Accelerators as start-up infrastructure for entrepreneurial clusters
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.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".