DO PUBLIC POLICIES FOR ENTREPRENEURSHIP MAKE A DIFFERENCE? PROSPECTIVE SCENARIOS FOR CANADA, IRELAND, AND ITALY <br><strong>DOI:10.7444/fsrj.v4i1.95</strong>
Bibliographic record
Abstract
Micro, small, and medium-sized enterprises (MSMEs) constitute the majority of businesses and a significant part of developed and developing countries’ Gross Domestic Product (GDP). This article presents a series of prospective scenarios that investigate the effects of public policies on entrepreneurship in Canada, Ireland, and Italy. Public policies for this sector can be classified as regulatory policies (e.g., laws for the entrance and exit of businesses, labor and social laws, property laws, tax laws, intellectual property laws, bankruptcy laws, and laws that affect the liquidity and availability of capital) and stimulus policies (e.g., promotion of cultural and educational programs to foster entrepreneurship and internationalization). Regulatory policies influence the business environment for MSMEs, and are generally designed to provide entrepreneurs with high growth potential (known as “gazelles”). Four scenarios involving the critical uncertainties surrounding political and economic integration and technological development are developed for each country. Each scenario is constructed based on public policies specific to each country. This article concludes that public policies are not a panacea capable of generating economic development, given that their effectiveness depends on other economic decisions and exogenous economic conditions. However, the absence of state intervention does not produce positive effects, even in the case of positive scenarios under exogenous conditions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".