Leverage points in engineering ecosystems: student industrial secondments in East Africa
Bibliographic record
Abstract
While the relative shortage of engineering practitioners in Africa has been reported as a major obstacle on the road to development, a significant number of existing engineering graduates still find it difficult to find employment in engineering fields. This dichotomy may be partially explained by the inability of local industries to absorb more skilled labour; a relative deficit (real or perceived) in the competency of local graduates in the ever-advancing areas of science, technology, engineering and mathematics (STEM); and/or a scarcity of opportunities to hone and demonstrate their competency to employers. To address the challenge of competency deficit, this study postulated that promoting effective engineering student industrial secondment (SIS) activities can be a leverage point in the engineering ecosystem by strengthening the linkages between engineering education, practice and employability. The study surveyed the history of engineering practical training in Tanzania, Kenya, Uganda and Rwanda, complemented with a pilot study of four long-term, employment-like SIS placements in Tanzania and Rwanda. The main objective was to observe closely, trial potential models, and learn from and synthesise effective SIS experiences. The study found similarities across the countries regarding experiences with student practical training models, their challenges, and the perspectives of stakeholders. Findings also support that longer durations of SIS placements than currently practised help increase the employability of engineering students. However, in view of the small number of placements, further evidence is called for.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".