“Rounding Up the Usual Suspects”: Exclusion, Selectivity, and Impunity in the Enforcement of International Criminal Justice and the African Union’s Emerging Resistance
Bibliographic record
Abstract
Abstract Despite the overwhelming ratification of the statute of the International Criminal Court (ICC) by African states, recent attempts to prosecute the perpetrators of egregious crimes in the region have come under a sustained opposition from its regional body, the African Union (AU). In fact, the blunt accusation is that international criminal justice has become an instrument of colonization. Within the context of the AU’s claim, this article engages the question of selective enforcement of international criminal accountability, ironically beginning with the Nuremberg trial. Without necessarily justifying the senseless perpetration of heinous crimes in Africa, this article argues that an international justice regime complex that is perceived to be skewed in favour of the West engenders a crisis of legitimacy and ultimately robs it of the much needed cooperation from the region.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| 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".