“Start‐a‐Business”: an experiment in education through entrepreneurship
Bibliographic record
Abstract
Purpose There is wide consensus on the importance of experiential entrepreneurship education. The purpose of this article is to investigate whether two unconventional experiential courses, with the style and content that the authors would like to have experienced before becoming entrepreneurs, can be successfully grafted on to the more conventional offerings of a large university business school. Design/methodology/approach The authors create learning by allowing a small group of students with serious business ideas to actually be entrepreneurs (rather than pretending to be) as they evaluate, optimize, and start running their businesses within the university course structure. All distractions from these goals, such as formal business plans and academic exercises, are removed, and direct contact with outside stakeholders is strongly emphasized. Fellow‐students and the instructor provide constant feedback and ideas to adapt and improve the businesses. Findings The courses meet a variety of accepted experiential education criteria, receive highly positive student evaluations, and generate many real businesses. Practical implications The methodology provides a practical, scalable, and effective way to provide university education through entrepreneurship. Originality/value The approach described in the paper has many unusual aspects and works very well. It may be of interest to others attempting innovations in the teaching of entrepreneurship and of the enterprising mindset.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".