Abstract
Criminal justice systems do not become fair just because they collect more data. What matters is what they measure and how they use it. My capstone project examines disproportionate minority contact in the Jefferson Area Criminal Justice System because we need a better way to identify where unequal contact happens and what structural conditions are tied to it. It expands earlier local work by looking beyond race alone and testing how neighborhood-level socioeconomic conditions help explain contact. My STS research asks how local sociotechnical practices, such as categories and decision tools, shape the disparities that later appear. I chose this because inequality is shaped by the way that the system defines and processes people. The capstone shows where disproportionate contact is concentrated, and the STS paper explains how technical and administrative practices help produce those outcomes.
My capstone project addresses how criminal justice disparities are often discussed broadly, how those conversations are weak when they are not tied to specific neighborhoods and actual system drivers. The project links Regional Jail booking data with U.S. Census tract data so criminal justice contact can be studied alongside economic factors. This required major data cleaning, addresses were standardized and linked to census tracts, then I used clustering and principal component analysis to identify the main structural patterns. This approach matters because it moves the discussion away from vague claims and toward a clearer picture of how neighborhood conditions and system contact interact.
The capstone’s main conclusion is that socioeconomic disadvantage is the strongest driver for variation, even though racial disparities remain clear. Higher booking rates were concentrated in disadvantaged, high-poverty tracts, while lower booking rates were concentrated in more affluent ones. Black residents were overrepresented in most tracts and especially concentrated in disadvantaged clusters, while White residents were overrepresented in affluent areas with lower levels of system contact. The findings also show that race’s relationship to criminal justice involvement depends heavily on neighborhood context and socioeconomic conditions. This means local reform cannot stop at broad claims about fairness. It must address the interaction between neighborhood disadvantage and criminal justice decision making.
My STS paper asks how local sociotechnical practices produce measured disparities in the criminal justice system. This matters because it pushes back on the idea that criminal justice data is just a neutral record of inequality. Instead, it treats disparity as something shaped through classification and decision making. I use Actor-Network Theory as the main framework, ANT treats both human and nonhuman elements as part of the same network, so officers, software systems, forms, and reporting standards all matter in explaining outcomes. The STS paper combines this framework with the local findings to show where disparities emerge and how definitions and routines shape what counts as contact.
The STS paper concludes that disproportionate minority contact is cumulative and built across multiple stages of the system rather than at one isolated decision point. Evidence shows racial disparity in charge seriousness, pretrial detention, and guilty outcomes, which suggests that unequal treatment builds through a chain of routines and classifications. Using ANT, I argue that these outcomes are shaped not only by individual discretion but also by the system’s infrastructure. The larger point is that criminal justice data is not a neutral mirror. It is part of the machinery through which inequality becomes visible and reinforced. Together, both projects show that real justice reform requires changing both human decision making and the systems that organize it.
