The Duty to Prevent Genocide under International Law: Naming and Shaming as a Measure of Prevention
Bibliographic record
Abstract
In contrast to prosecuting and punishing committed acts of genocide, the Genocide Convention is silent as to means of preventing future acts. Today it is generally accepted that the duty to prevent is legally binding, but there is still uncertainty in international law about its specific content. This article seeks to fill this gap in the light of the object and purpose of the Genocide Convention. It provides a minimum requirement approach, i.e. indispensable State actions to comply with their duty to prevent: naming and shaming situations of genocide as what they are. Even situations from times before the Genocide Convention was in force must be named and shamed today. Although the Convention is not retroactive, events from the pre-Convention era are relevant. They are necessary links to strengthen a general awareness what constitutes genocide and by that cater to the (also) legal purpose to prevent future acts of genocide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".