Managing Risks through Mundane Governance: Crisis Communication and the Making of Non-Incidents
Bibliographic record
Abstract
Much of the crisis communication literature focuses on acute, high-profile events that draw public attention. Yet many organizations routinely manage situations with the potential for controversy that remain largely unnoticed. Building on Woolgar’s concept of mundane governance, which highlights how governance is enacted through ordinary, everyday technologies, this paper explores how ‘cold’ crisis situations are governed and communicated in ways that help them remain ‘cold.’ Focusing on the example of nuclear safety inspectors in the Netherlands responding to incidents that could, in principle, trigger public concern, this study examines how these routine practices are carried out, documented, and communicated in ways that reaffirm their status as unremarkable. This examination includes how these non-events are selectively picked up or ignored by residents and local media. The paper highlights how risk assessment reports, classification systems, and press releases operate as tools of mundane crisis governance, enacting forms of risk communication that help calibrate public concern and reassurance. In doing so, they contribute to maintaining a sense of safety. By applying the concept of mundane governance to crisis communication, the paper offers a fresh perspective on how organizations manage potential crises, and how governance is performed not only in moments of visible crisis, but also – and perhaps especially – in their absence.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".