Assembling Pieces of Accountability for the Srebrenica Genocide
Bibliographic record
Abstract
The Srebrenica genocide has been the subject of multiple legal proceedings against various actors before different courts, at both the national and international level. Amongst others, the International Criminal Tribunal for the Former Yugoslavia has sentenced various individual perpetrators, the International Court of Justice has ruled on the responsibility of the Serbian state, and Dutch courts have been asked to rule on the liability of the Dutch state and the United Nations. This raises the following question: to what extent have multiple adjudicatory mechanisms across legal regimes managed to deliver accountability for the Srebrenica genocide and what are the remaining accountability gaps? In order to answer this question, the article focuses on both legal procedural accountability and substantive accountability. It first recalls the events of July 1995, including the different actors involved (both through their actions and omissions), before giving an overview of cases that have been litigated under criminal law, tort law, and international state responsibility law in multiple international, regional and domestic jurisdictions. The analysis concludes with an overarching analysis of the extent to which legal accountability for the Srebrenica genocide has been achieved.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| 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".