Regulatory and Risk Management Issues Arising in the Context of Experiential Entrepreneurship Education
Bibliographic record
Abstract
The Start a Business Assignment forms part of the experiential entrepreneurship curriculum in numerous universities. A lack of awareness of potential liabilities can create liability for students and the university from problems arising while operating businesses. Losses or damages potentially arise from both regulatory infractions and potential injuries to third parties. A lack of knowledge and understanding of the legal obligations imposed upon business operations threatens the learning experience. This article seeks to identify liability risks to the universities and faculty that can result from an improperly planned and supervised Start a Business Assignment. A clearer understanding of the legal issues that can arise will be helpful in supporting the safe growth and ongoing health of experiential entrepreneurship programming. Our aim is to support experiential entrepreneurship learning by concluding with a suggestion as to how these assignments can be managed in a manner that minimizes associated risks and adds to the student experience. Many of the legal principles have broad application, but it is critical to recognize that each city, state, and country's laws and their interpretation thereof may vary. Furthermore, the unique parameters of each Start a Business Assignment will affect the risks arising from regulatory and legal compliance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".