Entrepreneurial space and the freedom for entrepreneurship: Institutional settings, policy, and action in the space industry
Bibliographic record
Abstract
Abstract Research Summary Anticipating that innovation nurtures entrepreneurship, we began an extended case study of an innovative start‐up in the space industry. We quickly saw that institutions imposed formidable barriers to implementing entrepreneurship from innovation. Curious about how, why and the extent of this situation, we widened our study to other start‐ups, CEOs of existing businesses, an incubator, a technology transfer office and key influencers in large space companies and agencies. We found that institutions and policies had, in effect, shrunk the entrepreneurial field, leaving little room for enterprise. Conceptualizing from this, we propose the institutions create an “entrepreneurial space.” Theoretically, we explain how this concept of an entrepreneurial space can be usefully applied in other contexts. Managerial Summary The space industry is extremely innovative. It is also dominated by two powerful incumbent firms and a third that is highly regulated. This research examines how entrepreneurship in the space industry is shaped by institutions, and what this implies for the freedom to be entrepreneurial. We investigate this question in the French European context. We find that while the industrial context and institutions had completely pushed entrepreneurship out of the upstream segments it flourished in the margins of this industry. The upstream segment is not at all entrepreneurial; downstream is the entrepreneurial milieu of the space industry. We recommend that policymakers (a) strengthen private‐public‐partnership arrangements; (b) implement policies to attract venture capitalists to transform and reinvigorate the upstream segment; and (c) design specific incubation mechanisms for space start‐ups.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".