Developing Next Generation of Innovators: Teaching Entrepreneurial Mindset Elements across Disciplines
Bibliographic record
Abstract
The purpose of our project was to explore the explicit or implicit engagement of faculty members across the curriculum in teaching the entrepreneurial mindset. We begin by defining entrepreneurship on a spectrum, recognizing the contextual nature and psychological development associated with entrepreneurial thinking. We developed a self-report survey containing a combination of quantitative and qualitative items to determine faculty member knowledge of entrepreneurship and their engagement in teaching elements of the entrepreneurial mindset. We surveyed the faculty at a primarily teaching university in the western United States. Sixty-four faculty members (~20%) with representation from across the disciplines completed our survey. We found constrained knowledge of entrepreneurship, indications of teaching elements of the entrepreneurial mindset, and approaches to assignments that were limited in scope for fostering entrepreneurial thinking. The implications of our research are a need for professional development to enhance faculty members’ knowledge of entrepreneurial thinking and support for instructional and content choices that could enhance student development of an entrepreneurial 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".