Impact on SMR support by teaching low-dose radiation using infographics and social license in Canada
Bibliographic record
Abstract
Public understanding of low-dose radiation (LDR) is often limited, which may affect support for emerging nuclear technologies such as small modular reactors (SMRs). This study aimed to assess whether targeted infographics could improve LDR understanding and influence SMR support, interpreting the findings through the lens of the social license to operate (SLO). Two types of infographics were developed: one used natural analogies to compare natural and artificial sources of radiation, and the other focused on LDR protection procedures in hospitals and nuclear power plants. Their impact was evaluated using pre- and post-infographic surveys and focus group discussions. Results were analyzed using binary logistic regression, with infographic type, gender, age, income, and education as independent variables, and SMR support as the dependent variable. Both infographics increased support for SMRs in Alberta and Saskatchewan. However, in Ontario—the only province currently generating nuclear power—the natural analogy infographic led to a slight decrease in support. These findings are discussed within the SLO framework, emphasizing the need for context-specific communication strategies in nuclear policy engagement.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".