Bibliographic record
Abstract
The outbreak of violence in South Sudan in December 2013 has enlarged ethnic divides and reversed the development advances the young nation has struggled to achieve since independence; while the continued deadlock in the IGAD-led negotiation process provides little hope of a negotiated peace settlement. A peacebuilding strategy that takes into account the context-specific circumstances of this intra-state conflict enhances the opportunity for peace and development in South Sudan, and provides the international community an opportunity to contribute to peace in a meaningful way. Limited capacity, deep and persistent ethnic divisions, corruption and a long memory of brutality within the civilian population complicate prospects for peace in the country. An examination of recent peacebuilding efforts in South Sudan reveals a fragmented and provisional approach. Using the framework of Ali and Matthews, this paper outlines a peacebuilding strategy for South Sudan that addresses root causes, consequences and legacies of the conflict, while taking into consideration the unique country specific circumstances. Recognizing the need to move from negative to positive peace, this paper prioritizes security and political arrangements as essential prerequisites for success in economic development and justice and reconciliation. The role of the international community, regional/sub-regional organizations and global civil society.
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 | 0.002 | 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 | 0.020 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".