Prosecutorial Policy Indicators of the Prosecutor of the International Criminal Court
Bibliographic record
Abstract
The prosecutorial policy of the Prosecutor of the International Criminal Court (ICC) plays a central role in the realization of international criminal justice and in ensuring accountability for serious international crimes. This article aims to analyze the main indicators of the Prosecutor’s prosecutorial policy by examining the legal framework, judicial principles, and political, operational, and ethical indicators. An examination of the legal framework shows that the Prosecutor’s decisions must be based on legal authority, the principles of legitimacy and justice, and the limitations of the Rome Statute. Judicial indicators include prioritization of cases, analysis of evidence, and an emphasis on deterrent and preventive justice. In addition, political and international indicators—such as interaction with the Security Council and diplomatic pressures—can play a decisive role in the selection of cases, while operational and ethical indicators, including resource management, transparency, and respect for the rights of both defendants and victims, contribute to maintaining the legitimacy and effectiveness of prosecutorial policy. An analysis of practical examples demonstrates that the success of prosecutorial policy requires a balance between judicial effectiveness, prosecutorial independence, and adherence to human rights standards. The findings of the article emphasize that transparency, accountability, and ethical commitment are indispensable indicators in the design and implementation of prosecutorial policy, and that they can contribute to enhancing justice and legitimacy of the Court at the international level.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".