Bibliographic record
Abstract
Entrepreneurship is a driving force in today's rapidly evolving economy, offering avenues for innovation, economic growth, and societal impact. However, engineering entrepreneurial teams often encounter skill gaps that can impede their ability to navigate the complexities of starting and growing a business effectively. Addressing these skillset gaps is crucial for empowering the next generation of entrepreneurs to thrive in an increasingly competitive landscape. University-based incubators (UBIs) play a significant role in this endeavor, providing a supportive ecosystem for entrepreneurial development within academic institutions. This paper aims to explore the skillset gaps in UBIs affiliated with engineering departments, through a systemic literature review on this topic, complemented by a series of one-to-one interviews with directors, mentors, and investors at various UBIs. The results of this study align with existing literature and highlight a recurring theme regarding the difficulties faced by engineering entrepreneurial teams in areas such as soft skills, as well as challenges related to financial literacy, research proficiency, and sales capabilities. This work aims to delve into specific areas in greater depth and discusses potential pedagogical approaches that could enhance the learnings of these skills with no direct relation to concepts of entrepreneurship.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".