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Record W7116635200

Managing Risks through Mundane Governance: Crisis Communication and the Making of Non-Incidents

2025· article· en· W7116635200 on OpenAlexaff
Michiel Van Oudheusden, Keje Boersma

Bibliographic record

VenueVU Research Portal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsAthena Sustainable Materials Institute
Fundersnot available
KeywordsCorporate governanceCrisis communicationPerspective (graphical)Risk governanceRisk managementRisk communication
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.488
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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