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While data 1 has shown that COVID-19 disproportionately affects Black people, the CDC’s early data listed race as “missing/unspecified” at high rates. Incomplete demographic data obscures the virus’s full impact on marginalized communities. Without more information about who the virus is affecting and how, we cannot protect our most vulnerable. This article demonstrates disconnects between reported datasets and data visualizations in public-facing COVID health and science communication and suggests steps that technical and professional communicators can take in creating or using data visualizations accurately and ethically to describe COVID conditions and impacts.
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Stored only when both papers are in the corpus — 31 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.