Staging Knowledge Amongst Experts: How an Accelerator Works to Develop an Ecosystem
Bibliographic record
Abstract
Accelerators play a key role in entrepreneurial ecosystems, however, extant research is limited in knowledge and theory about how accelerators work to develop entrepreneurial ecosystems. Accelerators are the means by which ventures seek to gain knowledge to build high-growth ventures, but accelerators are also posited to contribute meaningfully to ecosystems in other ways. I employ field theory to conceptualize an entrepreneurial ecosystem as a field and accelerator events as field-configuring events. I engage in a two-year ethnographic study of a series of accelerator events to explore how an accelerator works to achieve ecosystem-level outcomes using events. I find that the accelerator engages in stage production practices to produce a knowledge refinement process amongst field-level experts. Contrary to existing research on entrepreneurial ecosystems, the accelerator engages beyond a simple facilitative role in knowledge transfer to purposeful engagement in a process of knowledge refinement. This paper contributes to research on accelerators in entrepreneurial ecosystems, as well as field theory by positing the accelerator as not only a host to field-configuring events but also an active influencer to the ecosystem field as a field-configuring organization.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".