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 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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".