Diplomatic Practices, Domestic Fields, and the International System: Explaining France’s Shift on Nuclear Nonproliferation
Bibliographic record
Abstract
France took a hardline stance against Tehran’s nuclear program. Yet for several decades, the French government adopted a softer line with regard to nonproliferation. France was the last permanent member of the UN Security Council to ratify the 1968 Non-Proliferation Treaty (NPT). In the 1960s and 1970s, Paris sold nuclear facilities to Israel, Iran, and Iraq, some of which had potential military uses. Why did France change its policy on nonproliferation? By reconstructing the evolution of France’s position with respect to Iraq’s and Iran’s nuclear programs since the 1970s, this article explains how Paris shifted from reluctant loner to one of the main promoters of the norm of nonproliferation. The changing role of a second-tier power stems from transformations in the division of diplomatic labor inside and outside the state. Drawing on archival and interview data, we show how a group of French diplomats, followed by political leaders and nuclear scientists, mobilized the nonproliferation norm and carved out a role for France in the international system. Throughout this period, successive presidents faced fragmented domestic fields in which bureaucratic and political struggles shaped French policy. These struggles refracted the impact of the international system on France’s slow and uneven convergence with the US position.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".