Risk, “Radiophobia,” and Social Learning: Applying Lessons from the Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it