Decades of development: A bibliometric analysis of small modular reactor research
Bibliographic record
Abstract
This study presents a comprehensive bibliometric analysis of global research trends on Small Modular Reactors (SMRs), based on 2080 peer-reviewed publications retrieved from Scopus and Web of Science as of February 2025. Through an analysis of keyword co-occurrence, publication sources, and the contributions of institutions and countries, the study identifies major research areas as well as emerging topics. Safety-related issues, including passive safety systems and natural circulation, remain dominant in the literature, while interest in next-generation reactor types, hybrid energy systems, and integration with renewables continues to grow. In contrast, non-technical dimensions such as public acceptance, policy frameworks, and waste management remain relatively underexplored. A country-level analysis shows that research output is concentrated in a few countries such as the United States, China, South Korea, and Canada, with leading institutions demonstrating topic-specific specialization. Network analysis confirms the centrality of safety-focused research while also identifying limited engagement with socio-political aspects. These findings suggest the need for interdisciplinary research and increased academic attention to issues such as economic feasibility, governance, and long-term waste strategies to support the successful commercialization of SMRs.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.150 | 0.259 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".