Nuclear Non-Proliferation and “Preventive Self-Defence”: Why Attacking Iran Would Be Illegal
Bibliographic record
Abstract
Summary Iran’s pursuit of nuclear technology continues to place a major strain on US–Iranian relations, with many US decision makers still sceptical of Iran’s claims that its uranium-enrichment program is aimed only at providing fuel for civilian purposes, not at developing nuclear weapons capability. In spite of the diplomatic progress made to date, the conclusion of a comprehensive agreement resolving the issue remains elusive, with powerful elements in both states resistant to any compromise, and the United States’ key regional allies, Israel and Saudi Arabia, strongly opposed to the Obama administration’s decision to negotiate with the government of Hassan Rouhani. Consequently, a US attack on Iran in order to (at least) severely delay Iran’s nuclear program remains a distinct possibility. After outlining the causes of the current situation, and noting the extent to which both Iran and the United States have disregarded their obligations under the Treaty on the Non-Proliferation of Nuclear Weapons, this article considers the lawfulness of a potential US military strike against Iran, examining in detail relevant international legal rules governing the use of force. The conclusion reached is that such a preventive use of force would be — and should remain — illegal and that adherence to their respective legal obligations still offers the best way forward for both countries.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".