NEGOTIATING NONPROLIFERATION: Scholarship, Pedagogy, and Nuclear Weapons Policy
Bibliographic record
Abstract
In nuclear nonproliferation negotiations, many governments pursue multiple objectives, and changes in policy can occur rapidly—and often unexpectedly. For these reasons, understanding nonproliferation requires empathy and imagination rather than just historical fact. This article considers one teaching tool to encourage such insight—simulations—and demonstrates how teaching and scholarship can interact to improve our understanding of the complex decisions and negotiations involved in nuclear nonproliferation. The article consists of five parts: first, it explains the benefits of simulations as both a policy development tool in Washington and as a teaching tool in universities; second, it describes the pedagogical strategy of the Stanford University simulation program; third, it shows how the simulations have identified and highlighted theoretical and substantive insights that are often neglected in scholarly studies of nonproliferation; and fourth, it describes how students are tested to enhance the learning experience from the simulation. Fifth and finally, the article provides concluding observations about how using simulations in the classroom can help scholars develop insights that improve their understanding of real-world nuclear negotiation dynamics and outcomes.
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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 | 0.008 | 0.015 |
| 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 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".