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Episode 162: How the "Data-Driven" Label Sanitizes Cruel Austerity Politics
Manage episode 330394568 series 1500148
“Follow The Data” is the name of a Bloomberg Philanthropies podcast that debuted 2016. “How Data Analysis Is Driving Policing,” a 2018 NPR headline read. “Data suggests that schools might be one of the least risky kinds of institutions to reopen,” an opinion piece in The Washington Post told us in the early days of the Covid-19 pandemic.
Over the last 20 or so years, a trend of labeling concepts as “data-driven” emerged. It applied, and continues to apply, to policies affecting everything from education to public health, policing to journalism. Decisions affecting these areas will be more thoughtful, the idea goes, when informed and supported by data. In many ways, this has been a welcome development: The idea that a rigorously scientific collection of information via surveys, observation, and other methods would make policies and media stronger seems unimpeachable.
But this isn’t always the case. While gathering “data” is a potentially beneficial process, the process alone isn’t inherently good, and is too often used to obscure important and requisite value-based or moral questions, assert contested ideological priors and traffic in right-wing austerity premises backed by monied interests. When our media tell us a largely unpopular, billionaire-backed idea like school privatization, “targeted” policing, or tax incentive handouts to corporations have merit they’re backed by “the data,” what purpose does this framing serve? Where does the data come from? Who is funding the data gathering? What data are we choosing to care about and, most important of all, what data are we choosing to ignore?
On today’s episode, we’ll look at the development of the push to make everything data-driven, examining who defines what counts as “data,” which forces shape its sourcing and collection, and how the fetishization of “data” as something that exists outside and separate from politics is more often than not, less a methodology for determining truth and more a branding exercise for neoliberal ideological production and reproduction.
Our guests: Abigail Cartus is an epidemiologist at Brown University. She focuses on perinatal health and overdose prevention in her work at The People, Place & Health Collective, a Brown School of Public Health research laboratory.
351 에피소드
Manage episode 330394568 series 1500148
“Follow The Data” is the name of a Bloomberg Philanthropies podcast that debuted 2016. “How Data Analysis Is Driving Policing,” a 2018 NPR headline read. “Data suggests that schools might be one of the least risky kinds of institutions to reopen,” an opinion piece in The Washington Post told us in the early days of the Covid-19 pandemic.
Over the last 20 or so years, a trend of labeling concepts as “data-driven” emerged. It applied, and continues to apply, to policies affecting everything from education to public health, policing to journalism. Decisions affecting these areas will be more thoughtful, the idea goes, when informed and supported by data. In many ways, this has been a welcome development: The idea that a rigorously scientific collection of information via surveys, observation, and other methods would make policies and media stronger seems unimpeachable.
But this isn’t always the case. While gathering “data” is a potentially beneficial process, the process alone isn’t inherently good, and is too often used to obscure important and requisite value-based or moral questions, assert contested ideological priors and traffic in right-wing austerity premises backed by monied interests. When our media tell us a largely unpopular, billionaire-backed idea like school privatization, “targeted” policing, or tax incentive handouts to corporations have merit they’re backed by “the data,” what purpose does this framing serve? Where does the data come from? Who is funding the data gathering? What data are we choosing to care about and, most important of all, what data are we choosing to ignore?
On today’s episode, we’ll look at the development of the push to make everything data-driven, examining who defines what counts as “data,” which forces shape its sourcing and collection, and how the fetishization of “data” as something that exists outside and separate from politics is more often than not, less a methodology for determining truth and more a branding exercise for neoliberal ideological production and reproduction.
Our guests: Abigail Cartus is an epidemiologist at Brown University. She focuses on perinatal health and overdose prevention in her work at The People, Place & Health Collective, a Brown School of Public Health research laboratory.
351 에피소드
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