Dark Side of the Moon: Sanctions, Security, and Nuclear Decision-Making in Iran
Bibliographic record
Abstract
Although the Treaty on the Non-Proliferation of Nuclear Weapons does not explicitly mention it, the idea that sanctions can be used as a tool to prevent the spread of nuclear weapons is not new. The earliest mention of it appears in the 1946 Baruch Plan, which suggested imposing a penalty on potential violators. Since the 1970s, sanctions have been used to discourage states from building nuclear weapons. Such sanctions were imposed at both bilateral and multilateral levels. The proposed paper aims to analyse the effectiveness of sanctions as a tool for disarmament and non-proliferation. While examining this broader point, this paper argues that an important yet often overlooked point in the sanctions literature is the end point of the sanctions. How would those who comply and end their programs to get out of sanctions be treated by the US and other actors imposing sanctions on them? How would they be treated post-sanctions? Would they be treated differently once they accept the conditions, alter their policy, and refrain from proliferating to avoid sanctions? What if the sanctioned state realizes that whatever it does, the sanctions will not be lifted? How would this realization affect their behaviour and resolve? In such cases, can sanctions be taken as an effective non-proliferation tool? President Donald Trump’s decision to pull out of the JCPOA and impose new and stricter sanctions against Iran in 2018, which the Biden administration maintains is a case in point. How did the Iranians view the JCPOA? And the American withdrawal? Did it reinforce the belief that only a nuclear weapon would guarantee Iran’s security? Using data collected through interviews and surveys, supplemented by data from the Toronto-based Iran Poll, this paper aims to answer these questions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".