Integrating Technopreneurship Education in Nigerian Universities: Strategy for Decreasing Youth Unemployment
Bibliographic record
Abstract
As of the third quarter of 2018, the rate of unemployment stood at 23.13 percent as revealed by the National Bureau of Statistics. In view of the foregoing, this study proposes technopreneurship education as an effective strategy that can be used to reduce unemployment. Sequential mixed-methods research designs (quantitative and qualitative) were adopted for the study. The population for the quantitative part consists of 300 level students of universities in Kwara State, while lecturers and artisans in the universities formed the population for the qualitative part. Stratified, quota and random techniques were used to select 370 respondents while purposive and convenience techniques were adopted to select two lecturers and artisan. Research instruments titled “Technopreneurship Education and Business Intention Questionnaire (TBQ) and “Interview Protocol on Technopreneurship Education and Business Intention Questionnaire (IPTEQ)” were used to collect relevant data. Findings from both quantitative and qualitative methods revealed that the three dimensions of technopreneurship education (i.e., entrepreneurship course, entrepreneurship practical and internet facility) were significantly related to business intention. Also, findings show that inadequate facility and financial constraint constitute challenges that hinder technopreneurship, while adequate facility and availability of funds were perceived as the remedies to the challenges. Based on the findings of the study, it was recommended that government, banks and other stakeholders in education should assist universities in terms of providing financial assistance to students with business intention.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".