The Implementation Formula of Entrepreneurship Education at Higher Education as a Solution for the Social Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims at finding out the implementation formula of entrepreneurship education at the university. The searching of electronic journal articles by using database such as: JSTOR, SAGE, Proquest, Elsevier, Emerald Insigth, and Google Scholar from 2009 until the end of October 2019. The keywords: entrepreneurship education, entrepreneurial and unemployment. The implementation formula of entrepreneurship education is done through the curriculum reformations and improvements, the creation of extracurricular activities and entrepreneurship programs, the empowerment of human resource development for entrepreneur lecturer (teachers), the adequacy of infrastructures and financial supports, the strengthening of cooperation with associates, the alumni empowerment and also the support of government. The implementation formula of entrepreneurship education at the university is able to shape the character, improve the habits, attitude, and the passions of the university students to be entrepreneurs and also increase the number of new entrepreneurs in Indonesia that will be a solution of intellectual unemployment problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it