Citizen science in the nuclear field: An exploration of its potential in governing nuclear incidents, accidents, and post-disaster situations
Bibliographic record
Abstract
Citizen science (CS) is a form of science developed and enacted by citizens, typically with citizen volunteers collecting and/or analyzing various kinds of data. As CS serves public purposes (e.g. educational goals) and emanates within democratic and participatory cultures (e.g. the open science movement), it potentially broadens scientific research and facilitates public participation in science policy. Whereas the role of CS is well documented in fields such as amateur astronomy, biohacking, video gaming, etc., there is a dearth of research about the role of CS in the nuclear field. Yet, following the 2011 Fukushima Daiichi nuclear disaster, CS has demonstrably contributed to filling knowledge and information gaps, as citizens in the affected areas monitor radioactivity in the environment and communicate about environmental risks (e.g. Citizens’ Radioactivity Monitoring Project). In this process, citizen scientists have voiced ardent criticism of government and industry, as these institutes are seen to deliberately inhibit open knowledge sharing. Taking these insights as an entry point, this paper probes the potential of CS in the governance of nuclear incidents/accidents, emergency situations, and in post-disaster recovery. Drawing on past and present CS initiatives connected to nuclear incidents and accidents in Japan, the USA, Canada, and the UK, it conceptualizes the social spaces in which CS emerges; ascertains which knowledge, information and decision-making challenges CS addresses; and determines which collective lessons can be drawn to ensure more legitimate and socially robust nuclear governance. Particular attention is given to the role governments, industries, and established scientists can, and should, assume as potential facilitators, patrons, or challengers of a more collective, open approach to disaster preparedness and response. The latter category comprises social scientists, who in Japan have been criticized for “disengaging” with CS practice, thereby limiting opportunities for contextual learning about disasters and even hampering post-trauma disaster recovery. The paper engages with the following conference themes: The future role of publics in processes of government/governance; Empowering publics in new innovation processes.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".