The Failed Plan : South Korea’s Pursuit of Nuclear Weapons and the U.S. Nonproliferation Policy in the 1970s
Bibliographic record
Abstract
This paper studies the conditions under which South Korea ceased its attempts to acquire nuclear weapons. It seeks to answer the question: How did Washington persuade Seoul to give up its nuclear pursuits? Existing literature has thus far only discussed the U.S. influences on South Korea, but it has not fully explained how U.S. nonproliferation policies effectively prevented South Korea from going nuclear. This paper examines what policies were most effective in discouraging the development of nuclear weapons. The U.S. approached Canada and France to create a negotiating environment that made it easier to implement its nuclear nonproliferation policy. Then the U.S. promised technical cooperation in the nuclear field in a form in which South Korea would not have the capability to build nuclear weapons. The history of how the export control policy was implemented while promising technical cooperation in the nuclear field provides us with an important case study in how it may be possible to persuade today's nuclear-weapons-developing nations to give up their nuclear weapons. This paper has salience today, as North Korea has become a de facto nuclear weapons state through its continuous nuclear testing, meanwhile, many South Korean people are in favor of their country developing nuclear weapons. Reviewing the historical process of U.S. nonproliferation policies that helped prevent the South Korean program will help us determine what policies could effectively prevent nuclear proliferation in East Asian countries in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".