Data citizenship
Quantifying structural racism in Covid-19 and beyond
Bibliographic Data
| ID | 5260710 |
|---|---|
| Authors | Cal Lee Garrett (0000-0003-0063-4807, University of Illinois Chicago, corresponding author), Claire Laurier Decoteau (0000-0001-5644-0361, University of Illinois Chicago) |
| Year | 2023 |
| Volume | 10 |
| Issue | 2 |
| Publication date | 2023-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/20539517231213821 |
| OpenAlex | W4388707782 |
| Language | EN |
| Citations received | 5 |
| References cited | 45 |
Data-driven public health policies were widely implemented to mitigate the uneven impact of COVID-19. In the United States, evidence-based interventions are often employed in "racial equity" initiatives to provide calculable representations of racial disparities. However, disparities in working or living conditions, germane to public health but outside the conventional scope of epidemiology, are seldom measured or addressed. What is the effect of defining racial equity with quantitative health outcomes? Drawing on qualitative analysis of 175 interviews with experts and residents in Chicago during the emergence of COVID-19, we find that these policies link the distribution of public resources to effective participation in state projects of data generation. Bringing together theories of quantification and biosocial citizenship, we argue that a form of data citizenship has emerged where public resources are allocated based on quantitative metrics and the variations they depict. Data citizenship is characterized by at least two mechanisms for governing with statistics. Data fixes produce better numbers through technical adjustments in data collection or analysis based on expert assumptions or expectations. Data drag delays distribution of public relief until numbers are compiled to demonstrate and specify needs or deservingness. This paper challenges the use of racial statistics as a salve for structural racism and illustrates how statistical data can exacerbate racial disparities by promising equity
Citizenship · Data collection · Health care · Health equity · Political science · Politics · Public health · Public relations · Qualitative property · Racism · Social science · Sociology · Statistics · Food Security and Health in Diverse Populations · Gender Studies · Law · Medicine · Public Health Policies and Education · Race, Genetics, and Society
Thicker than blood
Inclusion
The Economization of Life
The Republic of Therapy
The Seductions of Quantification
The Schematic State
Ancestors and Antiretrovirals
The Truly Disadvantaged
Racial Capitalism
Race Decoded
Disciplining the Poor
Sorting Things Out
Powers of Freedom
The Averaged American
Bridgework
The State, Legal Rigor, and the Poor
A genealogy of epidemiological reason
Towards a critical policy ethnography
Numerical operations, transparency illusions and the datafication of governance
Ordinal citizenship
Data deprivations, data gaps and digital divides
Social determinants of health in the Big Data mode of population health risk calculation
Racial formations as data formations
The Politics of Surviving
A Sociology of Quantification
Biological Citizenship
Migrants in Translation
Seeing Like a State
Figures of the Future
Standardizing Biases
Biopolitical bordering
Ordinalization
Flexible Coding of In-depth Interviews
Using model-based evidence in the governance of pandemics
Disease surveillance infrastructure and the economisation of public health
Introduction
Rankings and Reactivity
The Covid-19 Pandemic
| Unique citing works | 5 |
|---|---|
| Citations per year | 2,5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |