Managing the Improvement of Entrepreneurship Education Programs: A Comparison of Universities in the Life Sciences in Europe, USA and Canada
Bibliographic record
Abstract
In this chapter we contribute to the literature on the entrepreneurial university by focussing on research-based interventions to implement or improve the entrepreneurship education program. To this end, a benchmark study is executed in a specific domain of the life sciences in Europe, USA and Canada: the agri-food sciences. The research question of this chapter is: What kind of research-based educational interventions can be formulated for managers at universities in the life sciences who want to start with or improve their (high-tech) entrepreneurship education program? The main results of this study are that six dimensions of entrepreneurship education are identified – based on the FORA and NIRAS reports - and further developed through a literature review. These dimensions are: strategy, resources, institutional infrastructure, education, outreach and development. Based on the literature review and the benchmark study, specific educational interventions for each dimension of entrepreneurship education could be identified and described. These interventions enable managers of life science universities to start with or improve their entrepreneurship education program.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".