Omnipresent Threats: A Comment on the Defence of Duress in International Criminal Law
Bibliographic record
Abstract
This article argues that in the context of international criminal law, the defence of duress must be considered where an actor is compelled to commit a crime as a result of a sufficiently serious threat – even if the form of that threat is not explicit or direct and the pending harm will not necessarily occur within a specific period of time. Drawing on the current conflict in Syria to exemplify our argument, we advocate for an approach that allows consideration of the many environmental factors that may cumulatively create an ‘omnipresent threat’ that should not be disregarded by the criminal justice system. We propose that duress should be considered where the actor held a genuine and reasonable belief that she faced a sufficiently serious threat and that commission of the offence was the only way to escape the harm. We urge that Article 31(1)(d) of the Rome Statute be interpreted accordingly.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.046 | 0.047 |
| Insufficient payload (model declined to judge) | 0.002 | 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".