Use of Career Education and Occupation Information Services in Boosting Enrolment into Vocational and Technical Education Programs in Nigeria
Bibliographic record
Abstract
The major thrust of technical and vocational education (TVE) worldwide is to address issues of youth unemployment, poverty and international competitiveness in skills development towards current and projected opportunities and challenges. The paper posits that despite the evidence of numerous reforms in vocational and technical education programs over the years, they still remain greatly detested by students and parents in Nigeria as evidenced in low enrolment when compared with sciences, engineering, banking and finance, and medicine just to mention but a few. This may be because of lack of understanding of the importance, scope and content of the program or lack of occupational information. Some wrongly view technical and vocational education programs as education for the handicapped or education for those who cannot cope with the sciences and social sciences. This paper therefore, examines the factors responsible for low enrolment into technical and vocational education programs, its implications for Nigeria and suggests how career education and effective occupational information service can help to stem this trend. It is recommended that career education should be used to create the necessary career awareness and occupation information made readily to students to enable them make informed career choices.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".