Canadian Nascent Entrepreneurs' Start-Up Efforts: Outcomes and Individual Influences on Sustainability
Bibliographic record
Abstract
Of the people who attempt to start a business, how many actually bring their venture to fruition? Until now, the answer to this question has eluded researchers, because of the difficulty in identifying and contacting people in the gestation phase of business start-up. In overcoming this sampling challenge, the research upon which this article is based tracks the start-up efforts of 151 Canadian nascent entrepreneurs (individuals engaging in activities to start a business from scratch) over a two-year period. In addition to providing new insights into the dynamics of small business births and deaths, the paper explores the role individual-level factors play in sustaining efforts to start a business. While finding no significant differences in personal background factors (socio-demographic, work, and career backgrounds) within the sample, certain aspects of personal context and personal predispositions were shown to differentiate those who disengaged from the start-up process from those who persevered. Problem-solving style and goal orientation were especially significant. The implications of the findings are discussed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".