Archetypes of Pedagogical Innovation for Entrepreneurship in Higher Education: Model and Illustrations
Bibliographic record
Abstract
Observing the dearth of research-grounded discussions on the quality of pedagogical innovations in entrepreneurship education, and more specifically, on what makes pedagogical innovations ‘work’, we develop an analytical framework that highlights the core characteristics of pedagogical innovations, and the coherence relationships between these characteristics. We illustrate the import of the framework by analyzing four innovations in entrepreneurship education from four institutions in four different countries: the Oregon State University’s Austin Entrepreneurship Program (USA); the Master in Management Global’s Parcours Entrepreneuriat from l’Universite Paris-Dauphine (France); the High-TEPP initiative from the Universities of Bamberg, Jena and Regensburg (Germany), and the University of Victoria’s Entrepreneurship Program (Canada). By analyzing these cases, we show that from the diversity of initiatives in entrepreneurship education, one can identify at least four archetypes of innovative practices. More importantly, we develop a research-grounded framework that can be used to study the similarities and differences between different pedagogical innovations in entrepreneurship education, but also to evaluate their degree of internal coherence. In turn, we provide a practical tool for entrepreneur educators to reflect upon their own innovative practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".