South Sudan: stakeholders’ views of technical and vocational education and training and a framework for action
Bibliographic record
Abstract
The Republic of South Sudan, recently emerging from the longest civil war in contemporary African history, has set goals towards post-conflict reconstruction in many areas of social services. However, the educational infrastructure continues to struggle, and many stakeholders in government and international and local organisations are not sufficiently aware of the needs, challenges and opportunities that face the implementation of technical and vocational education and training (TVET) in the country. Preparing future generations of youth and adults with in-demand technical skills and retraining ex-combatants to enter a peacetime workforce is essential to the development and growth of South Sudan. As a first step towards creating the foundation necessary for post-conflict training, we collected and analysed qualitative data from focus groups, in-depth interviews, field observations, and archival documents and identified three interrelated elements that require attention for the effective development of TVET: political climate, curriculum and delivery options. The resulting findings offer a starting point for addressing some of the key constraining factors for the important job of TVET development in South Sudan.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".