Bibliographic record
Abstract
Abstract Variation in entrepreneurial ecosystems between places is as much due to the different varieties of actors as to the varieties of institutional forces. However, we know less about the individual and organizational actors who constitute entrepreneurial ecosystems, and most current work has focused on the types of resources and institutions in thriving ecosystems. To fill this research gap, this chapter introduces an actor/role configurations perspective to understand the kaleidoscope of regional entrepreneurial ecosystems. A role is a set of functions an actor takes on that contributes to the ecosystem’s development, functioning, or reproduction. To investigate the multiple roles that ecosystem actors take on and how these roles shift across different economic and cultural contexts, we analyse eighty semi-structured interviews with technology entrepreneurs in three Canadian entrepreneurial ecosystems: Waterloo, Calgary, and Ottawa. We find that in entrepreneurial ecosystems, actors take on multiple roles, but these actor/role configurations depend on varied local contexts. This demonstrates that entrepreneurial ecosystems are not homogeneous entities; rather, regional contexts lead to different actor/role configurations, creating a kaleidoscope of entrepreneurial ecosystem forms.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".