Exploring the Effects of Entrepreneurial Extracurricular Activities on Student Entrepreneurs
Bibliographic record
Abstract
Academic entrepreneurship is becoming more accessible and comprehensive, and with it, so too are entrepreneurial extracurricular activities (EEAs). Because the expansion of entrepreneurship education accessibility is limited to the last two decades, little has been explored on the exact impacts EEAs have on students enrolled in entrepreneurship education. This thesis focuses on expanding the recent body of knowledge in what qualities EEA participants develop and how they plan to use them in their futures. A comparative analysis between the ecosystems of two leading entrepreneurship academic institutions was conducted, with one being Lund University in Sweden and the other being Carleton University in Canada. The results obtained suggest that entrepreneurship students from a plethora of previous prior backgrounds can, as students, enlist in EEAs available to them and experience benefits typically at the cost of time, a commodity with a volatile price to student entrepreneurs. The findings continue by pointing out that the same diversity of possible EEA learning scopes may suitably match the variety of enlisting student entrepreneurs, in turn, allowing for qualities across all disciplines intersecting with entrepreneurship to be learned, though characteristics such as culture, nationality, and sex, may yield a negative impact on learning outcomes.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".