The entrepreneur, wealth creator and growth seeker: myth or reality?
Bibliographic record
Abstract
Nowadays, entrepreneurship appears as the driver of social and economic development (Observatory of European SMEs, 2003). Entrepreneurs are perceived as the pillars, if not the saviours, of market economy, and their activities as creating value, employment and multiple advantages for consumers. However, some recent studies (Shane, 2009) show that the entrepreneur seen as a hero would be nothing else than a myth. Most of the time, entrepreneurs would create ordinary firms that would not grow. Although the entrepreneur has largely been ignored by classical and neo-classical economic theory, the myth of the entrepreneur as a wealth creator finds its roots in the postulate of profit maximisation that would force firms to grow. However, actual competition, combined with the fact that, often, ownership and management are in the hands to the same people, allows entrepreneurs to pursue utility functions other than profit maximisation. Entrepreneurs often follow non-monetary aims like independence, power, or achievement. This leads them to voluntarily limit growth. The aim of this paper is thus to explain how this myth was born and to show that, if these alternative objectives are not taken into account, it is not possible to reconcile the entrepreneurial behaviour with apparent infringements to economic and management theories.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".