How European Governance Shapes Entrepreneurial Ecosystems to Unlock SME Innovation
Bibliographic record
Abstract
The purpose of this study is to analyze the influence of public governance on the effectiveness of European supranational public policies in fostering innovation within innovation ecosystems (IEs), specifically through the lens of the PITCCH initiative, a European innovation project funded by Horizon 2020 under the 'Innovation in SMEs' measure. Utilizing a 10-month ethnographic field study, this research examines how public governance mechanisms provide targeted, results-oriented support to IEs, with a particular focus on innovation-driven small and medium enterprises (i-SMEs). The findings reveal that public governance plays a critical role in facilitating the interaction between i-SMEs and larger ecosystem stakeholders, thereby enhancing the innovative capacity and market value of i-SMEs.This research contributes valuable insights into how public governance strategies can be effectively deployed to stimulate economic and scientific growth within IEs, offering practical guidance for policymakers and implications for future policy development. The also contributes to academic discourse by providing empirical evidence on the nuanced roles of public governance within European supranational contexts.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".