Bibliographic record
Abstract
This chapter discusses the role of entrepreneurship in creating new jobs and innovating new products. Both of these topics have their origins in research conducted in the late 1970s and the 1980s, when David Birch first claimed that small new firms acted as the engine of job creation in the economy, and David Audretsch and Zoltan Acs argued that small firms played a disproportionate role in the commercialisation of new innovations. Both topics command widespread interest because they suggest that entrepreneurship directly drives venture performance and economic growth. Much (though not all) of the empirical discussion in this chapter is framed in terms of comparisons between small and large firms. The focus on firm size – which, as noted in chapter 1, does not obviously capture the essence of entrepreneurship – is chiefly a historical legacy. It also reflects data availability. In the discussion that follows, ‘small business’ will merely be assumed to serve as a convenient shorthand for entrepreneurship. However, this focus will be complemented with a discussion about the role of individual entrepreneurs in job creation and innovation. After setting out in the first section some basic facts about entrepreneurs' decisions to hire external labour, I will present some theory about the labour demand of individual entrepreneurs. This paves the way for an analysis of the empirical factors which determine job creation by entrepreneurs. The second section discusses the role of small firms in creating jobs in the broader economy.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".