A Field in Flux: Trends and Opportunities in Entrepreneurship Education
Bibliographic record
Abstract
The past decade has seen a massive increase in entrepreneurship education at the graduate and undergraduate level. Yet, despite the flowering of new classroom and co-curricular offerings, key questions remain in terms of what entrepreneurship means and how it can (or should) be translated to academic contexts. For example, prior academic research examines the impact of classroom education on venture formation, yet many educators today emphasize entrepreneurship in terms of mindset and critical thinking. How might this broader conceptualization affect the design and delivery of classes? Similarly, questions around appropriate topics and templates, emerging technologies and experiential learning, integration with broader campus ecosystems, and more broadly how to promote “frame-breaking” behavior in a traditional classroom context are hotly debated in the literature. In this symposium, we bring together a group of entrepreneurship scholars and educators to examine the state of a field in flux, with the goal of facilitating a discussion among scholars engaged in studying or practicing entrepreneurship education.
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.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".