Technical Education, Vocational Training and Industrialisation in Sub-Saharan Africa(SSA)
Bibliographic record
Abstract
This study contributes to a deeper understanding and perspective on the current debate on structural transformation in Sub-Saharan Africa (SSA) by investigating the effect of technical vocational education and training on industrial performance between 1980 - 2018. The panel data used for this study were obtained from World Development Indicators (WDI), International Labour Organization (ILO), United Nations Educational, Social and Cultural Organization (UNESCO) and Fraser Institute databases. The empirical results derived from the Instrumental Variable (IV) Two-Stage Least Squares (2-SLS) econometric approach highlighted the important role of Technical Vocational Education and Training (TVET) as key determinants of industrial performance in SSA. The study found strong and robust relationship between TVET and measures of industrialisation. General secondary education, on the other hand, had a negative effect on industrialisation in SSA. The paper recommends therefore that there is the need for a complete overhaul and revision of the educational system in SSA with more emphasis on TVET in order to meet the required labour demand for industrial needs in the foreseable future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".