Strengthening States’ and the International Community’s Responsibility to Protect Civilians: Revisiting the Prosecution of War Crimes Committed in Africa by the International Criminal Court (ICC)
Bibliographic record
Abstract
Abstract The silhouette of International Criminal Justice (ICJ) is fast changing across the globe. The change and transformation are connected to the criminalization of war, which has complicated the attraction of and engagement in the war for war-mongers. At least, the last few years had seen remarkable prosecution of war criminals in Africa. This is related to a relatively new thinking that informed the establishment of International Criminal Court (ICC) and global re-enforcement of war crime-related charges. Since the genocide in Rwanda, the establishment of the ICC has led to the prosecution of warlords. Also, the ICC has issued thirteen public warrants of arrest on war charges to actors and perpetrators in more than four African states. The case of President of Sudan, whose warrant of arrest had been issued regarding the crisis in Darfur, demonstrated that African leaders and war-mongers would be held responsible for their actions and atrocities they have committed. The lesson from the ICC is clear, war-mongers would be made to pay for their criminality. This article intends to examine the actions of the ICC on intra-state civil war crimes in Africa and assess whether ICC can act as deterrence on for intrastate war mongers in Africa.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".