The Place of TVET as a Tool for Manpower Development for Achieving Vision 20; 2020 in the Nigerian Construction Industry
Bibliographic record
Abstract
In demonstration of its commitment towards achieving the Millennium Development Goals (MDGs), the Federal Government of Nigeria has set year 2020 as the target year of becoming one of the 20-leadingeconomy globally: termed Vision 20:2020. However, Technical and Vocational Education Training (TVET) is one of the training strategies adopted for the required manpower development to drive the economy towards achieving this laudable vision. This Study therefore, examined TVET as a means of manpower development required for attaining Vision 20: 2020 in Nigeria. The major objective is to determine the effectiveness of the programme (TVET) as a verifiable tool for building the necessary manpower to drive the economy towards achieving the set vision. Towards this end, a questionnaire survey was conducted on a sample of one hundred (100) establishments that engages HND graduates in Quantity Surveying in one year youth service Scheme covering the six geo-political zones in Nigeria. The data obtained were subjected to Relative Skill Acquisition Index (R.S.A.I). The RSAI obtained was compared with the expected RSAI of 4 (Good Performance) using Chi-square (c2) test at 95% confidence level. SPSS 15.0 version was adopted for the analysis. The result revealed that the obtained RSAI is significantly lower than the expected (?<0.05). The paper concluded that the skilled acquired by the graduates is far below the required skill that will drive the economy towards achieving the vision.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".