Archetypes of Pedagogical Innovation for Entrepreneurship in Higher Education: Model and Illustrations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it