THE INTERNATIONAL CRIMINAL COURT IN GUINEA: A CASE STUDY OF COMPLEMENTARITY
Bibliographic record
Abstract
The International Criminal Court (ICC) and domestic criminal justice systems work together to prosecute the worst crimes through the principle of complementarity. This principle, enshrined in article 17 of the Rome Statute, holds that the Court will only intervene if a State is either unwilling or unable to investigate crimes falling within the Court’s jurisdiction. Since the entry into force of the Rome Statute, complementarity has evolved with practice. The Office of the Prosecutor now adopts a practice of “positive complementarity”, meaning that, more than simply wait on the sidelines to determine whether a State is both willing and able, the Court now takes an active role in helping a State fulfil its Rome Statute obligations. Positive complementarity treats as permeable the border between the ICC and States, through which expertise, coordination, and documentation pass in an effort to end impunity. But there are various ways in which the Court and States can work together in this process. The current practice leans on international networks and actors to help a State investigate and prosecute. While the addition of these actors can channel resources toward the aim of justice, competing aims of the players within these networks can make for a confused picture at best; at worst they can tear open large impunity gaps that no prosecutorial strategy could tolerate. The case of Guinea forebodes these gaps and provides important lessons on how they may be closed through a more proactive approach to complementarity—such is the focus of this article.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".