Leveraging Entrepreneurship Education in Italy’s Inner Areas: Implications for Regional Planning
Bibliographic record
Abstract
This paper examines how place-sensitive, transdisciplinary entrepreneurship education can catalyze inclusive innovation in peripheral regions. Drawing on the Pathways to Innovation and Entrepreneurship initiative—implemented in Southern Italy through a collaboration between the University of Naples Federico II and Cornell Tech with the support of the US Diplomatic Mission to Italy—this study explores the role of universities as active agents in regional innovation ecosystems. Adopting an action research methodology across inner and peri-urban territories, the initiative combined transdisciplinary learning, international knowledge exchange, and applied innovation to support regional planning. Findings highlight three interdependent causal pathways: (1) experiental learning and the development of transversal competencies, (2) network formation across scales, and (3) context-sensitive innovation practices. The results show how a locally embedded yet globally networked approach contributes to innovation capacity building of peripheral regions, aligning global knowledge flows with territorial strengths. The paper concludes with implications for embedding EE into regional innovation strategies, fostering diverse network management, and promoting sustainable, place-based development in left-behind places.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".