Research

How do states decide which relationships are "family"? Political science scholarship mostly treats the family as either pre-political or as a site of politics, leaving little discussion of how states constitute family. Addressing this gap, this paper develops the Intimacy Contract, a theory arguing that states selectively recognize relationships and extend entitlements (e.g., inheritance, child custody) to those that resemble the state’s ideological vision of family while dismissing those that do not comport with this vision, regardless of comparable interdependence. I illustrate this theory by analyzing over 6,000 cases involving relationships that lack default legal recognition and whose legitimacy is socially contested: women in extramarital relationships seeking spousal entitlements in India, and non-biological caregivers seeking parental rights in the U.S. I show that courts recognize relationships when they resemble the state’s ideal family. These findings demonstrate that states constitute family, expanding our understanding of state power.

Many social scientists see the institutionalization of monogamous marriage as promising an egalitarian society. This essay challenges this view by arguing that the institutionalization of monogamy is exclusionary, extending and withholding recognition and entitlements on an ideological basis. I illustrate this argument by analyzing the selective recognition of extramarital relationships across 2,721 Indian appellate court cases from 2000 to 2024. When these relationships resemble the state’s ideological vision of monogamous marriage—a conjugal life, a traditional gender dynamic, and shared religiosity—courts are almost 35 percentage points more likely to recognize them and extend entitlements (e.g., spousal support, protection against violence) than when these features are absent. The gap highlights that women in comparable circumstances have uneven access to entitlements due to the ideological logic underlying monogamy, a system that excludes even as it promises equality.

This article examines the use of large language models (LLMs) to collect and classify information in court data. While computational approaches have traditionally relied on pattern matching techniques, these methods struggle to obtain detailed information from court data. Drawing on projects involving more than 2,700 Indian court cases and 4,800 U.S. state cases, I argue that LLMs offer three advantages over traditional approaches: they can identify concepts from natural language descriptions, synthesize information across complex documents, and lower the technical barriers to large-scale text analysis. I develop a framework to use LLMs effectively so that the model draws on relevant information and produces reliable outputs. In a hand-coding validation of 600 cases, the framework produced classifications that mostly aligned with human coding. The article concludes by discussing the ethical considerations, including environmental costs, research labor implications, and risks of concentrating power within a small number of firms.