Bibliographic record
Abstract
South Sudan is one the largest recipients of official development assistance. Given the complexity of the operational environment, there is a need to learn from the lessons gained to-date. This article seeks to enable better-informed decision making based on a synthesis from humanitarian and development evaluation reports, which offer insight for engagement in other fragile and conflict-affected states. Experimental methods were utilised to identify evaluation reports. The synthesis finds that projects would be better designed if they allocated time and resources to obtain additional information, integrated systems thinking to account for the broader context, and engaged with the gendered nature of activities and impacts. Implementation can be strengthened if seasonality is taken into account, if modalities are more flexible, and if a greater degree of communication and collaboration between partners develops. Sustainability and long-term impact require that there is a higher degree of alignment with the government, longer-term commitments in programming, a recognition of trade-offs, and a clear vision and strategy for transitioning capacities and responsibilities to national actors. While actors in South Sudan have been slow to act on lessons learned to-date, the lessons drawn from evaluation reports in South Sudan offer direction for new ways forward, many of which have been concurrently learned by a diverse set of donors and organisations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.165 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".