Ethical Principles for Common Ground in Science Communication Theory and Practice (Roundtable): Reflections on Risk Communication in the Aftermath of the 2011 Fukushima Nuclear Disaster
Bibliographic record
Abstract
In science communication, sharing information with peers and broader audiences is often taken as a moral value, along with principles like transparency, participation, and honesty. Yet, it is also acknowledged that there are limits to sharing knowledge or making it publicly available. Some types of information (e.g., personal data, security-related information) remain classified for long periods of time – and often with good reason. High-risk organizations (e.g., nuclear power plants, airports, emergency departments) in particular can be reluctant to share sensitive information at all, at the risk of being accused of ‘holding back’ vital information from the public in the event of an incident or accident. This is what happened in Japan in 2011, when the Japanese authorities failed to communicate reliable radiation data to the public in a timely manner, pushing ordinary citizens to collect and analyze their own radiation pollution data. In this presentation/roundtable discussion, I draw on this example of citizen science in radiation protection and my experiences as an embedded sociologist at a nuclear research facility to consider how governmental agencies and research institutions can make themselves a more trusted (or less unreliable) information source for the public at large. Now that the public is increasingly becoming involved in citizen science initiatives and new ways of communicating science, agencies and institutes can no longer wish citizen science away. They must develop new principles, tools, and infrastructures that distinguish themselves from citizen scientists, whilst also forging alliances with citizen groups. My reflections build on work by Joan Leach and Fabien Medvecky on the ethics of science communication (https://link.springer.com/book/10.1007/978-3-030-32116-1).
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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 | 0.081 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.083 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.025 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 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".