EXPLORING THE NATURE AND IMPACT OF GESTATION-SPECIFIC HUMAN CAPITAL AMONG NASCENT ENTREPRENEURS
Bibliographic record
Abstract
This article explores the nature and impact of gestation-specific human capital on successful start-up among a random sample of Canadian nascent entrepreneurs. Although much is known about the relationship between individual-level factors and the probability of becoming a nascent entrepreneur, the same cannot be said for the relationship between individual-level factors and success in starting a business. Previous studies of existing business founders indicate that general human capital (education and work experience) plays a role in opportunity identification, but at best plays a very weak role in opportunity pursuit. In light of these findings we sought to identify elements of human capital that would be specific to gestation–previous start-up experience, completion of classes or workshops in starting a business, and financial management capability (FMC). In documenting these elements, we found the majority of the sample had not taken any classes or workshops on starting a business, were novices to the start-up process, and were characterized by a wide range of financial management capability. Among those nascent entrepreneurs who succeeded in starting a business, FMC was found to be associated with sustainability. We conclude by discussing implications for researchers.
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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 | 0.006 |
| 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 | 0.001 |
| 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".