Bibliographic record
Abstract
On April 11, 1994, approximately two thousand men, women, and children were brutally murdered by armed Hutu extremists after a group of Belgian UN peacekeepers abandoned the school facility where they had sought refuge upon the outbreak of the Rwandan genocide. Almost a quarter of a century later, the Brussels Court of Appeal (Court) on June 8, 2018 concluded the civil proceedings lodged by a number of Rwandan survivors and relatives against the Belgian commanding officers and the Belgian state. Overturning an earlier judgment of the Brussels Court of First Instance, the Court held that the decision to retreat from the facility was imputable only to the United Nations, to the exclusion of the Belgian authorities. Accordingly, the claims against the Belgian state were unfounded. The events—which inspired the movie Shooting Dogs (2005)—bear obvious similarities to the role of the United Nations Protection Force's (UNPROFOR) Dutch battalion (Dutchbat) in the evacuation of the Potoçari camp and the ensuing genocide of seven thousand Bosnian men and boys by Bosnian Serb forces in Srebrenica in 1995. Like the Dutch judgments in the (more well-known) Mothers of Srebrenica proceedings, the Mukeshimana appellate judgment provides a rare national court precedent that considers the imputability of the conduct of peacekeepers to troop-contributing countries. The Mukeshimana judgment, however, raises a high bar for finding such imputability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".