The Q-NPT: Redefining Nuclear Energy Governance for Sustainability
Bibliographic record
Abstract
Global peace, security, and sustainable energy development depend on effective nuclear energy governance. While the Nuclear Non-Proliferation Treaty (NPT) has served as a cornerstone in this domain, it faces challenges such as trust deficits, inequitable access to nuclear technologies, and regional instability. This paper proposes the Qudrat-Ullah Nuclear Peace and Trust (Q-NPT) framework, a dynamic implementation roadmap designed to address these issues. The framework focuses on fostering trust among stakeholders, ensuring equitable access to nuclear technologies, and promoting inclusivity in governance structures. A key theoretical contribution is the integration of trust-building measures with sustainable energy transitions, highlighting nuclear energy’s role in decarbonization and global energy security. The paper outlines actionable pathways for implementing the Q-NPT framework, including enhanced oversight by the International Atomic Energy Agency (IAEA), capacity-building initiatives, and training programs to enable safe and sustainable nuclear cooperation, particularly in developing nations. By operationalizing nuclear programs through this approach, the Q-NPT framework aligns nuclear energy governance with global sustainable energy objectives.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".