Effect of Entrepreneurial Education on Unemployment Reduction among Students in Nigeria
Bibliographic record
Abstract
The study examined the effect of entrepreneurial education on unemployment reduction among students in Chukwuemeka Odumegwu Ojukwu University, Igbaria. The problem of the study is as a result of increasing rate of unemployment in Nigeria. The study was anchored on Human Capital Theory and risk taking theory. As a cross-sectional survey research design, a structured instrument developed by the researcher to reflect such options as strongly agree, agree, undecided, disagree and strongly disagree popularly referred to as five (5) points likert scale was used to obtain information from the respondents. The population of the study was limited to final year students of Business Administration and Entrepreneurship Studies Department in Chukwuemeka Odumegwu Ojukwu University, Igbariam. The total population was 195. Business administration was 128 while entrepreneurship was 67. The study used convenience sampling technique in selecting the sample size for the study based on convenience and easy accessibility to the respondents. Research hypotheses were tested using Multiple Regression Analysis (MRA) which was carried out with the aid of Statistical Package for Social Science (SPSS). Findings from the study revealed that Skill acquisition has significant effect on unemployment reduction, Entrepreneurship empowerment affects unemployment reduction, Infrastructural development has significant effect on unemployment reduction in Anambra State. The study recommended that Entrepreneurial education in tertiary institutions should be practical oriented rather than theory as this will exposed the students to various lucrative skills. Government should provide a means of getting loans by small scale business owners in order to enhance the activities of small scale business in Nigeria.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".