Topical Analysis of Nuclear Experts' Perceptions of Publics, Nuclear Energy, and Sustainable Futures
Bibliographic record
Abstract
Nuclear energy experts consider commercial power from fission to be a strong contender to help mitigate the increasing effects of climate change, in part due to its low-to-no carbon emissions. Nevertheless, nuclear energy's history, including meltdowns such as Three Mile Island, Chernobyl, and Fukushima, and dumping in sacred Indigenous land such as Yucca Mountain, raises important concerns in public deliberation over nuclear power. These communicative dynamics are crucial to study because they inform larger conversations in communication scholarship about the role of experts in scientific controversies and the complicated nature of public trust in and engagement with science. Thus, this study explores the perspectives of experts and how they make sense of their own communicative practices through a topical analysis of semi-structured interviews with 12 nuclear scientists and engineers in the United States and Canada. Our analysis revealed four major topoi: (1) risk and safety, (2) government and policy, and (3) public education and engagement, and (4) cost, along which nuclear experts make sense of science-public boundaries and their role as scientists and scientist citizens. This paper extends our understanding and how scientists view themselves as communicative actors and the barriers and opportunities for how we can foster productive technical-public relationships around climate change solutions.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".