Nuclear technology: the "Thin Line" between weaponization and peaceful uses
Bibliographic record
Abstract
In the context of globalization and energy transition, sophisticated communications enable an easier access to nuclear related knowledge, material, and technologies. These changes make the work of responsible authorities such as the International Atomic Energy Agency (IAEA) in monitoring and regulating nuclear facilities more difficult. With the help of the Weaponization Score Index, a tool explicitly created within this paper, this study hopes to demonstrate that while the existing nuclear legal framework efficiently limits and prevents potential nuclear proliferation risks through a full range of legal agreements, a country with an advanced civilian nuclear program, if wanted, can easily transition from peaceful use of nuclear technology to manufacturing nuclear weapons. To do so, nine countries were strategically chosen: Pakistan, Canada, Iran, South Korea, Germany, South Africa, Saudi Arabia, and Ghana, with regards to their civilian nuclear program position. Based on 16 relevant drivers, among them: Human Resource Development, Nuclear Fuel Cycle, and Engineering and Design, the Weaponization Score Index enables a classification of the nine countries in four categories of matter that are Dormant, Latent I, Latent II, and Limited Capabilities. Pakistan, used as reference, reached the highest score of 54. Results of this study showed that countries such as Iran, Japan, Germany, South-Korea or South-Africa, classified into Dormant (40-54), possess most of the required capabilities to operate this transition. In order to thicken the line between peaceful uses of nuclear technology and weaponization, potential solutions will be presented in conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".