European Entrepreneurship Reinforcement Policies in Macro, Meso, and Micro Terms for the Post-COVID-19 Era
Bibliographic record
Abstract
In today’s unprecedented transformation in the global socio-economic system caused by the COVID-19 pandemic crisis and the escalating fourth industrial revolution, reinforcing innovative entrepreneurship appears a significant policy objective that can lead to overall socio-economic development. In this drastically changed context, entrepreneurship support policies seem that they need to be both conceptually and practically readjusted, simultaneously at the macro, meso, and micro levels. This paper investigates the case of public entrepreneurship policies in the European Union (EU), aiming to find specific patterns and suggest a new multilevel policy framework. Initially, the article offers a brief overview of the related trends created in the emerging post-COVID-19 era. Next, the “competitiveness web” perspective in terms of “macro-meso-micro” level synthesis is presented, considering that it can function as a theoretical framework for entrepreneurship reinforcement. Recent EU entrepreneurship support policy guidelines are then explored, emphasizing the latest trends and the development opportunities arising with the EU Recovery and Resilience Facility establishment to deal with the consequences of the current health and socio-economic crisis. Upon this basis, the paper concludes in a proposal for an integrated “macro-meso-micro” policy, placing at the epicenter the mechanism of the Institutes of Local Development and Innovation (ILDI). This policy aims to strengthen the spatially-located firms to reposition and readapt the “Stra.Tech.Man” potential they have and activate in their local business ecosystem (strategy-technology-management synthesis).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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".