Innovative start-ups and local development: an investigation of the relevance of entrepreneurs’ age in rural vs. urban areas
Bibliographic record
Abstract
Multi-faceted approaches are mandatory to advance entrepreneurship research. This is especially true when scholars investigate the effect of entrepreneurship on local development. In this case, scholars usually rely on the nexus ‘entrepreneurial profile/context’. In line with the above and the principles of the European Agricultural Fund for Rural Development (EAFRD), a European policy aiming to support the development of rural areas, this paper investigates whether, and to what extent, innovative start-ups launched by young vs. older entrepreneurs (specific profiles) contribute to local development of rural areas (a specific context) in Italy. Stochastic frontier analyses, based on 13,385 observations retrieved from the website of the Italian Ministry of Enterprises and Made in Italy – IMEMI, reveal that local development generated by the Italian start-ups varies according to the entrepreneurial profile and context. Specifically, statistical results show that the entrepreneurial profile is more relevant than the context. The nexus ‘entrepreneurial profile/context’ is pivotal for defining new tools and policies able to support local development and, undoubtedly, it requires even more research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".