Development of education programmes in South Sudan
Bibliographic record
Abstract
The aim of the thesis was to make a thorough research in finding facts and causes of educational disparities that had occurred between northern Sudan and south Sudan, and what could the thesis work suggest for future events that might be identical to the past situation, particularly in the South Sudan as a Republic. \n \nIt was essential for the thesis work to start by addressing questions such as Were all getting a chance to education in South Sudan? How much were studed E learning? What was the situation of the schools and service delivery? Would there be similar educational marginalization to some certain parts of South Sudan as was when the South was under the rule of the Sudan's government? What· is the country investing in education and how was it using these resources? Were the resources well deployed and managed to ensure efficient functioning of education system? \n \nThe additional goal of the thesis was to provide investigative guideline that a holistic suggestions that may help in educational development particularly in the newly born country of South Sudan, the guideline that may be used to encourage activists and socially responsible organizations operating in the country and the countries that have been the hosts (such like, Finland, Canada, the USA, Australia) of the South Sudanese people who have been forced to leave country during the civil war.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".