Untangling Sudan’s Discord: Decrypting the Intricate Threads of Turmoil
Bibliographic record
Abstract
The conflict in Sudan between its military forces and a rival paramilitary group, exacerbated by allied militias, has escalated into a dire humanitarian crisis, reminiscent of past civil wars where hundreds of thousands perished. This ongoing struggle, marked by thousands of casualties and millions displaced, centers on a power struggle between the Sudanese Armed Forces (SAF) and the Rapid Support Forces (RSF), with global powers seeking to influence the outcome. Despite initial hopes for democracy following the ousting of former dictator Omar al-Bashir in 2019, political turmoil ensued, culminating in a failed transitional government and the assumption of power by General Abdel Fattah al-Burhan. Despite agreements aimed at civilian-led transition, missed deadlines and the contentious integration of the RSF into the national armed forces perpetuate the conflict. Amidst this turmoil, international sanctions target entities funding the conflict, such as Alkhaleej Bank, Al-Fakher Advanced Works, and Zadna International, among others, reflecting broader efforts to disrupt funding sources and facilitate a democratic transition. In this context, this research delves into the underlying factors driving the conflict in Sudan. Received: 05-19-2024 Revised: 05-27-2024
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".