Western Gunrunners, (Middle-)Eastern Casualties: Unlawfully Trading Arms with States Engulfed in Yemeni Civil War?
Bibliographic record
Abstract
Abstract According to the United Nations Secretary-General, Yemen today constitutes the worst man-made humanitarian crisis in the world. It is fuelled by extensive third-state involvement, with none of the warring parties championing respect for international human rights and humanitarian law (to put it mildly). Conversely, primary rules of international law already prohibit arms transfers from the moment there is a significant risk that they could be used to commit or facilitate grave breaches, with the recipient’s past and present record of respect for international law qualifying as the crucial factor to predict future transgressions. From that perspective, it appears deeply disingenuous for western states to continue transferring military equipment to members of the multilateral coalition in Yemen while maintaining adherence to the international legal framework. This article thus aims to examine whether the legal framework lives up to its noble goals or rather serves to defend state decisions that primarily serve their economic interests. It is structured as follows: Section 1 starts with an overview of the facts, and the focus and aim of this article. Section 2 then sets out the international legal framework as it applies to the trade in conventional arms with states that are involved in a non-international armed conflict. Section 3 analyses key domestic judgments (in the UK, Canada, Belgium and France) to test the available facts against the legal framework as elaborated. Finally, Section 4 concludes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".