Risk, “Radiophobia,” and Social Learning: Applying Lessons from the Literature
Bibliographic record
Abstract
Social learning aims to produce a change in both understanding and behavior on the part of individuals that diffuses to wider social units and communities of practice. This paper asks: What lessons from the social learning literature can be applied to research and public engagement with respect to radiation exposure risk? Five key lessons were assembled, and recent survey results were used to demonstrate how these lessons can be applied to outline a risk communication strategy that includes, but is not limited to, well-designed engagement. The marked divergence between public and “expert” opinion on radiation exposure risk remains at the heart of current debates over the role of nuclear energy in tackling climate change. Earlier literature tended to be dismissive of the risk gap, siding with the experts and branding the public “radiophobic.” We show how applying the findings of the literature review to the design and analysis of the survey can overcome shortcomings of past approaches and build on strengths. This paper seeks to demonstrate the importance and interrelated nature of mixed-methods studies where quantitative and qualitative analysis is combined. This includes avoiding overly binary approaches of study and finding ways to open up conversations and exchanges. This exploration of social learning and public engagement highlights the potential barriers nuclear energy faces in contributing to the future energy mix and challenges current practices to be more perceptive to the spectrum of public positions to radiation exposure risk.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".