Entrepreneurial Education in Higher Institutions and Economic Development
Bibliographic record
Abstract
This study undertook an appraisal of entrepreneurial education in higher institutions and the correlation to youths’ economic empowerment national economic development. It was prompted by the problem of growing rate of unemployment amongst the country’s youth population especially the young graduates. The objectives sought were to examine the current rate of youths’ unemployment and the implications on entrepreneurship adoption and Nigeria economic development; determine the key drivers of Asian Tigers economic growth from the 1960 to 2000 and the role of education; and ascertain the extent entrepreneurial education in higher institutions could boost Nigeria’s economic development. The study adopted a combination of survey and desk research. Data analysis was qualitatively and quantitatively done. The quantitative was through Spearman’s correlation coefficient. Results obtained reveal that the high rate of youths’ unemployment and low rate of entrepreneurship adoption by the youths have significant negative effect on the nation’s economic development. The key drivers of economic growth of the Four Asian Tigers between 1960 and 2000 were sound government policies on entrepreneurial, technical & vocational education. That entrepreneurial education in higher institutions can significantly boost Nigeria’s economic development. It was then recommended among other things that: The Nigerian youths must as matter of urgency take entrepreneurship much more serious, as a veritable complement to their educational attainment and as a surety for future greatness in the corporate world, and consequent boosting of the nation’s economy.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".