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 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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".