Nationwide entrepreneurship content in secondary schools: Impact on entrepreneurial careers
Bibliographic record
Abstract
Abstract Research Summary In an agenda setting seminar in 1997, when entrepreneurship was just emerging as a serious field of scholarship, Nobel Laureate Kenneth Arrow offered a challenging null hypothesis, namely, that entrepreneurship being a stochastic phenomenon, educational content is unlikely to make a difference in startup rates or success thereafter. With a view to begin tackling this null, we utilize a nationwide educational reform as a quasi‐experiment to investigate the effect of business and entrepreneurship‐oriented education on (i) early age startup (i.e., direct effects) and (ii) postgraduation choices that may lead to startup activity later (i.e., indirect effects). We find evidence for positive direct and indirect effects and discuss implications for future research into the design of entrepreneurship education policies as well as content and teacher training. Managerial Summary A variety of entrepreneurship programs exist at prestigious universities to stimulate startup for students who enroll in these based on entrepreneurial preferences and intentions. The resulting new ventures created are important drivers of innovation, economic growth, and job creation. Our study indicates that exposure to business and entrepreneurship content at an earlier age is equally important. Both in terms of obtaining a realistic insight into entrepreneurship as a career, leading to more realized startups of high quality but also in terms of developing a preference for entrepreneurship affecting subsequent choices regarding tertiary education and employment in favor of future entrepreneurship. We suggest investments in broadening entrepreneurship education for all in contrast to for specific targeted groups such as university or technology entrepreneurs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".