In the Shadow of International Criminal Justice: The Impact of The International Criminal Court (ICC) on Addressing Atrocity Crimes in Situation Countries: A Case Study of Uganda and Kenya
Bibliographic record
Abstract
There has been considerable scholarly attention on the International Criminal Court (ICC) since it came into operation nearly two decades ago. Most of this literature has focused on the role of the Court in the global justice system, particularly its relationship with states. It has been argued that through complementarity – the recognition of states’ primacy over the ICC in exercise of jurisdiction within their territories – the Court may exert pressure on states to prosecute international crimes by acting as a catalyst for domestic action. These theoretical assumptions have been followed up with empirical studies on the effects of complementarity on ICC situation countries. Complementarity-focused research has, therefore, sought to determine whether threatened or actual intervention of the ICC has spurred domestic prosecution of international crimes. While this has been the main focus of studies on the impact of the ICC and certainly provides useful data, there has been limited attention on assessing the impact of the ICC from a holistic perspective which would entail understanding the Court’s broader ‘shadow’ and how that shadow has shaped narratives of conflict and how to redress it. Even though the ICC’s broader purpose beyond its role as a criminal court is contested, this research engages with key documents, policies and decisions of the Court to problematise the Court’s identity, on one hand, and empirical data from selected case studies to assess how the Court has been ‘received’ in the local set up, on the other hand. Further to understanding this ‘dialogue’, this thesis proposes a new framework of looking at impact through the political relationship between the ICC and the Situation country. This framework is then applied in explaining why impact may differ from one Situation to another.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".