Gatekeeper or Ally? Theorizing the Equity Work of Entrepreneur Support Organizations
Bibliographic record
Abstract
Entrepreneurial ecosystems have been found to embed structures that limit full participation of women and minority entrepreneurs. Yet, little is known about what actors in the ecosystem may do to drive change toward more diversity, equity and inclusion (DEI). Drawing from qualitative data of seven entrepreneur support organizations (ESOs) in Canada including 66 semi-structured interviews with multiple stakeholders, this paper develops the concept of equity work in entrepreneurial ecosystems. Adopting a social-symbolic work perspective, we find that ESOs engage in discursive and material work to construct different DEI narratives, namely representation, systems-change and laissez-faire narratives, to legitimize their DEI work. Yet, we also find that the impactful work occurs in relationships between different actors in the ecosystem. Our study contributes to the nascent stream of research on DEI in entrepreneurial ecosystems, clarifies the power of ESOs, and proposes to conceptualize entrepreneurial ecosystems as a social-symbolic system.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".