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Short abstract

To stop an emergency becoming a catastrophe, deision‑makers, technical experts and citizens require instant “situational awareness”. COVID‑19 dashboards promised such immediacy, yet their unavoidable mediation reveals the inherently political dimension of making emergencies actionable.

Long abstract

Within the security rationale of response, decision‑makers, technical experts and citizens need a shared “situational awareness” of an ongoing emergency to prevent it from turning into a catastrophe. Throughout the COVID‑19 pandemic this awareness was largely supplied by web‑based dashboards that visualise real‑time epidemiological data as graphs, tables and thematic maps. While these dashboards became central representations of the crisis, their neutrality and quality have been contested from the outset.

Drawing on STS research on scientific representations and Critical Data Studies, I shift the focus from the “correctness” of the data to the inevitable political dimensions of emergency knowledge production. A case can appear on a dashboard only after it has travelled through a long chain of mediators—data collectors, modelers, software developers, visual designers—each imposing specific constraints and decisions.

Based on 26 semi‑structured interviews with dashboard creators and deploying situational analysis (Clarke, Friese & Washburn 2018), I map these more‑than‑human constellations and expose the contingencies, choices, and socio‑technical limitations that shape pandemic data capture and visualisation. The insight that no unmediated, direct knowledge of an unfolding emergency exists shows that what counts as good emergency knowledge depends on its intended use and the tolerable distortions and ambiguities. Although the empirical case concerns COVID‑19 dashboards, the theoretical contribution is directly transferable to other planetary emergencies—most notably climate crises—where the mediation of proximity, urgency and political stakes similarly determines whether data become actionable or remain abstract.