Bibliographic record
Abstract
Previous studies have affirmed the importance of entrepreneurship education in developing, motivating, and empowering entrepreneurs especially ex-students cannot be over emphasized, because it has led to the positive increase in the existence of entrepreneurs, which, to a great extent, is responsible for the creation of more job opportunities and the reduction of unemployment. Looking at the increasing rate of unemployment, entrepreneurship education has helped in developing entrepreneurs after the classroom, which greatly culminates into curbing the problem of unemployment in in various economies. This study investigated the extent to which entrepreneurship education and its impact in the reduction of unemployment using ex-students four Universities (two private and two public) as a case for the study. With the aid of questionnaire as a data collection instrument, 150 ex-Students currently running their businesses were randomly selected. The Statistical Package for Social Sciences (SPSS) was used to analyze their responses. Findings revealed that the catastrophic problem of unemployment can to a great extent be reduced through educating, motivating, developing, and empowerment of students as entrepreneurs. It also shows that there is a strong relationship between entrepreneurship education and unemployment reduction as most intentions were translated into actions. The results of this study further revealed that practical entrepreneurship classes have helped in motivating students to become entrepreneurs after graduation. Considering these findings, it is recommended that entrepreneurship education, motivation, development and empowerment should be taken serious by the educational institutions in developing economies. And students at all levels should be encouraged to engage in entrepreneurial activities in order to further reduce the rate of unemployment in developing economies. This will enable them to be employers instead of jobseekers.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 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".