The Role of Social and Human Capital Among Nascent Entrepreneurs
Bibliographic record
Abstract
This study examined nascent entrepreneurship by comparing individuals engaged in nascent activities (n = 452), after screening a sample from the general population (n=30,427). Due to the large sample size and the utilization of a control group of non-entrepreneurs (n=608), the findings of this study present a new approach to the relationship between human capital, social capital and entrepreneurship. Our primary objective was to help close the significant research gap regarding the sociological characteristics of nascent entrepreneurs, as well as to examine the comparative importance of various contributions and factors, such as personal networks and business classes. Having friends in business and being encouraged by them was a strong predictor regarding who among the general population eventually engaged in nascent activity. The study fails to support the role of formal education in predicting either nascent entrepreneurship or comparative success, when success is measured in terms of the three defined activities — creating a business plan, registering the business, or obtaining the first sale. Of particular note was that attending business classes specifically designed to promote entrepreneurship failed to be associated with successful business paths. This research suggests that national governments considering intervention activities might be wiser to focus on structural relationships than on programs specifically targeted to promote certain entrepreneurial activities. The facilitation of entrepreneurial social capital should be more successful if agencies filter their assistance through previous existing social networks. In addition, our findings suggest that countries that lack a very highly educated population may not be at a particular disadvantage regarding entrepreneurial activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".