Trust, Equity, Transparency and Inclusion in Nuclear Energy Governance: Empirical Synthesis of the Q-NPT Framework
Bibliographic record
Abstract
The Qudrat-Ullah Nuclear Peace and Trust (Q-NPT) framework offers a governance model for nuclear energy that foregrounds trust, equity, transparency, and stakeholder inclusion. This paper provides an empirical synthesis of Q-NPT by integrating quantitative and qualitative evidence from recent nuclear energy studies and situating the framework within global policy contexts. The findings indicate that legitimacy in nuclear governance depends not only on technical and regulatory compliance but also on social trust, distributive fairness, and active stakeholder inclusion. The analysis further demonstrates Q-NPT’s applicability to emerging technologies—including microreactors and blockchain-based fuel management—highlighting its adaptability to contemporary governance challenges. Together, these insights advance Q-NPT from conceptual articulation toward an evidence-informed, socially robust foundation for legitimate and ethical nuclear energy governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".