Reflections on Entrepreneurial Learning in Tunisian Universities
Bibliographic record
Abstract
Entrepreneurship education helps develop entrepreneurial behavior by stimulating students’entrepreneurial skills. Indeed, students can acquire and implement specific methodologies to create, develop and support new activities. Our study will aim to describe and explain the entrepreneurship education in the Tunisian university. This learning is organized around five key areas: the methodology of entrepreneurship; training seminars; socio-economic development; support entrepreneurship and entrepreneurial values. By following a hypothetical-deductive and descriptive method, we try to know the influence of exogenous variables that will foster entrepreneurship learning and improve the intent and the entrepreneurial skills of students. In this study, we focus on student populations of 3 years at the university (called LMD in Tunisia) after the program; namely, entrepreneurship awareness and developing a business plan. The choice of this frame is explained by the fact that these students are just a few months away from integrating into the world of work and express a variety of professional career intentions. Our research provides theoretical and practical contributions. In fact, it offers tools to advance the practice of entrepreneurship education at the University of Tunisia, with the aim of promoting the emergence of the entrepreneurial initiatives of students and graduates of the University.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".