Mainstream or incidental phenomenon overlooked by public policy? the case of Quebec
Bibliographic record
Abstract
Nowadays, the entrepreneurial process frequently takes on complex pathways, and an increasing number of individuals combine wage employment and venture creation. In this way, so-called hybrid entrepreneurs can test their business ideas at their pace on a small scale before deciding to shift to full entrepreneurship. The hybrid entrepreneurial path has proved to be beneficial for enterprises’ survival and innovativeness.Yet, wage employment and entrepreneurship have been mostly considered mutually exclusive, people belong to one or another category. Unsurprisingly, policy makers seem to overlook hybrid entrepreneurship, focusing their attention on the more general self-employed population or on high-growth potential ventures. Drawing on data from the 2015 Quebec Entrepreneurial Index and prior research, I argue that overlooking hybrid entrepreneurship contravenes the interest of public policies to foster innovative and sustainable enterprises.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".