Accelerator expertise: <scp>U</scp> nderstanding the intermediary role of accelerators in the development of the <scp>B</scp> angalore entrepreneurial ecosystem
Bibliographic record
Abstract
Research Summary : To understand the intermediary role of accelerators in the developing regional entrepreneurial ecosystem of Bangalore, we analyze data from 54 interviews with accelerator graduates, accelerator managers, and other ecosystem stakeholders and from 49 websites, 13 online video interviews, 26 online news sources, and 301 pages of policy documents. Specifically, we adopt a socially situated entrepreneurial cognition approach to theorize how accelerator expertise, existing at a meso‐level, intermediates between (micro‐level) founders and the (macro‐level) ecosystem. In our model, four types of accelerator expertise—connection, development, coordination, and selection—together increase stakeholders’ commitment to the entrepreneurial ecosystem, leading to venture validation (success or failure) and ecosystem additionality. These findings indicate that accelerators contribute to ecosystems in a way that is distinct from, but supportive of, building individual ventures. Managerial Summary : Accelerators are a new form of entrepreneurial support organization. These organizations typically focus on developing individual start‐ups, but we find that they also help develop entrepreneurial ecosystems. They do so by acting as a bridge between start‐ups and the broader entrepreneurial environmental resources by: (a) helping form connections, (b) helping develop individual start‐ups, (c) helping coordinate the right match among the various players in the ecosystem, and (d) helping select mentors and founders with the appropriate motivation and knowledge. As these accelerators apply this expertise in this go‐between role, they help build commitment to the broader ecosystem. Furthermore, they enable success (or fast failure) of individual start‐ups and do so in a way that develops the overall entrepreneurial capacity of the broader entrepreneurial ecosystem.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".