From conception to inception: Initial findings from the Canadian Study on Entrepreneurial Emergence
Bibliographic record
Abstract
The purpose of the research upon which this paper is based is to develop a better understanding of how new firms emerge. This article presents preliminary findings from the initial stage of a three year study of nascent entrepreneurs/intrapreneurs (respondents starting a business from scratch) in Canada. Based on data from 127 start-up efforts, the individuals, the activities they are pursuing and the organization of these efforts during gestation are profiled. With over three-quarters of total job growth between 1989 and 1996 attributed to small businesses (Statistics Canada, 1997), it is not difficult to understand why the small and medium-sized enterprise (SME) sector is being fully recognized for its role in the economy today. Enthusiastically embraced by the popular press, this sector also is garnering increased attention from policymakers, academics and consultants to name a few. David Birch's seminal work in the United States during the 1970s did much to generate awareness that employment growth was arising from the inception or expansion of firms (Birch, 1987). Generally, a high rate of new firm formation makes an economy more diversified and builds capacity to cope with the uncertainty and complexity of the socio-economic change being
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".