Entrepreneurship Development Program in the Higher Education in Indonesia
Bibliographic record
Abstract
Since 2012 University of Manado (Unima) collaborates with Humber Business School Canada to develop an applied entrepreneurship education program. This program aims to change the existing entrepreneurship curriculum at University of Manado which has tended to focus on the theory to be more applicative. In addition to changing the curriculum of entrepreneurship courses, a student entrepreneurship center called Unima Humber Entrepreneurship Center (UHEC) is also created that helps entrepreneurial students in extra-curricular form by providing training, seminars and incubation of entrepreneurship, mentoring and coaching clinic, also conducting marketplace or expo. In the period of 2017-2019 Unima received three years grant from Ministry of Research and Higher Education trough Entrepreneurship Development Program. By making use of qualitative approach, this study would like to analyze the impact of the program in the University of Manado. The results showed that more than 900 students applied to be the candidates of the Entrepreneurship Development Program training, 835 students involve in entrepreneurship expos, and 60 form 835 students elected as trainees and they are active in the Entrepreneurship Development training program. Twelve of 60 students now have been released from the business incubator and have been independent business start up. The conclusion of this research is Entrepreneurship Development Program have positive impact in Applied Entrepreneurship Development in Unima.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".