Issues

Offering a critique is often easier than proposing a solution. This refrain echoed frequently in the months and years that we spent developing what would become the Systematic Content Analysis of Litigation EventS (SCALES) Open Knowledge Network, a project born from the collaborative efforts of experts across various disciplines.

Federal court records have been available online for nearly a quarter century, yet they remain frustratingly inaccessible to the public. This is due to two primary barriers: (1) the federal government’s prohibitively high fees to access the records at scale and (2) the unwieldy state of the records themselves, which are mostly text documents scattered across numerous systems. Official datasets produced by the judiciary, as well as third-party data collection efforts, are incomplete, inaccurate, and similarly inaccessible to the public. The result is a de facto data blackout that leaves an entire branch of the federal government shielded from empirical scrutiny.

In this Essay, we introduce the SCALES project: a new data-gathering and data-organizing initiative to right this wrong. SCALES is an online platform that we built to assemble federal court records, systematically organize them and extract key information, and—most importantly—make them freely available to the public. The database currently covers all federal cases initiated in 2016 and 2017, and we intend to expand this coverage to all years. This Essay explains the shortcomings of existing systems (such as the federal government’s PACER platform), how we built SCALES to overcome these inadequacies, and how anyone can use SCALES to empirically analyze the operations of the federal courts. We offer a series of exploratory findings to showcase the depth and breadth of the SCALES platform. Our goal is for SCALES to serve as a public resource where practitioners, policymakers, and scholars can conduct empirical legal research and improve the operations of the federal courts. For more information, visit www.scales-okn.org.

Most civil cases settle. Yet generating a definitive settlement rate presents complex definitional and empirical problems, both in what should count as a settlement and how to count it. This Essay makes three contributions to better understanding and defining settlement. First, we propose a flexible, empirically informed, operationalizable definition of settlement as party resolution. Second, we exploit a new federal litigation data source to count party resolutions using machine learning models trained on 11 million docket sheet entries. Third, we offer new findings on party resolution frequency and distribution in the federal courts. Settlement is more widely and differently deployed than previously understood. We uncovered almost 40,000 additional party resolutions that were missing from the main existing source of administrative data on federal litigation. We also observe party resolution appearing alongside other dispositive events, functioning as a trimming device to drop parties, claims, or both as a lawsuit proceeds. We conclude by mapping directions for future work, integrating our settlement findings into a larger discussion of litigation and the courts’ role in achieving resolution, whether by a judge, jury, or the parties themselves.

Two Latin phrases describing litigants—pro se (for oneself) and in forma pauperis (IFP, as a poor person)—prompt this inquiry into the relationship between self-representation and requests for filing fee waivers. We sketch the governing legal principles for people seeking relief in the federal courts, the sources of income of the federal judiciary, the differing regimes to which Congress has subjected incarcerated and nonincarcerated people filing civil lawsuits, and analyses enabled by SCALES, a newly available database that coded 2016 and 2017 federal court docket sheets. This Essay’s account of what can be learned and of the data gaps demonstrates the challenges of capturing activities in federal lawsuits and the burdens, unfairness, and inefficiencies of current federal court waiver practices.

The U.S. criminal legal system is notoriously racialized. Though Black and Latinx people make up less than 30% of U.S. residents, they constitute more than 50% of the nearly two million people currently in U.S. prisons and jails. For decades, research has indicated that one group of decision-makers has had an outsized influence on these numbers: prosecutors. From whom to charge to what sentences to recommend, no actor plays a greater role in determining who goes to prison in this country. Highly subjective and lacking in formal guidance and accountability, prosecutorial decisions are especially vulnerable to racial bias. They are also cloaked in secrecy. Data about how and why prosecutors make decisions often does not exist or is shielded from public view. As a result, it has been nearly impossible to determine the extent to which prosecutors’ decisions contribute to racial disproportionality in the criminal legal process, let alone whether such decisions are the product of racial bias.

This Essay argues that prosecutors’ offices must collect, maintain, and publish standardized data on the bases of their charges, declinations, plea offers, and resolutions if we are to ever understand and address vectors of racial bias in the criminal legal system. Contextualizing this “call for data” within two case studies—one on the racialized impact of felony murder and accomplice liability murder laws and the other on the California Racial Justice Act—we demonstrate how prosecutorial data transparency would enable researchers, advocates, and policymakers to better identify and remediate racial bias in decision-making. Data transparency would also promote prosecutorial accountability both internally and externally.

Legislative efforts to implement data transparency must address privacy and surveillance concerns, especially since prosecutorial data transparency would expand a carceral source of information. At the same time, the consequences of data opacity are already shaping case outcomes. In this way, data transparency provides one remedy for currently unchecked systems, and serves as a step towards data justice.

Prior to the 2022 midterm elections, we conducted large-scale randomized controlled trials in Iowa and Washington aimed at increasing voter turnout among newly enfranchised individuals with past felony convictions. Alongside national and grassroots partners, we designed and implemented experiments to ascertain the effectiveness of alternative outreach mechanisms, including targeted mailers and digital ads. We did not detect statistically significant or economically meaningful effects on voter registration or turnout; most observed effects were precise nulls. The absence of measured impact is likely attributed to low digital engagement with our online ads as well as extensive voter outreach already conducted by our local partners prior to the study. Our evidence highlights the importance of context in voter outreach efforts, as the political and legal environment in Iowa and Washington differed significantly from other regions where similar interventions had previously shown success.