This chapter introduces Governance by Emulation, a framework analyzing how public law models, particularly administrative and constitutional mechanisms like individual rights adjudication, are reproduced in private and regulatory governance. Focusing on corporate-controlled content moderation, it examines the European Union’s out-of-court dispute settlement bodies (ODSs) under the Digital Services Act and Meta’s Oversight Board–conceptualized as Emulated Guardians. These institutions borrow the legitimacy of courts to regulate novel, bureaucratic private power structures while addressing public demands for accountability. Grounded in law, sociology, and political science, the chapter outlines the book’s methodology and contributions. It delves into four inquiries: the actors involved, their tasks, the power they seek to discipline, and how public law principles are adapted for private governance. These dynamics highlight emulation’s duality: it promises innovation yet risks performative legitimacy devoid of substantive reform. By situating Emulated Guardians within broader global governance challenges, this chapter frames content moderation as a microcosm of future issues in sectors like AI, biotechnology, and space exploration. It concludes that while governance by emulation addresses urgent accountability demands, its efficacy depends on public engagement and institutional evolution, offering a critical lens to assess emerging accountability structures beyond state control.

Qualitative and quantitative methods can help make sense of the range of meanings connected with concepts, mapping usage and clarifying alternatives so people can position their intended meanings with respect to the space of existing alternatives. Likewise, qualitative and quantitative methods can help clarify where measurement problems come from, and can be used together to turn abstract ideas about measurement error into far research conversations about how social dynamics work to produce social science measures in the first place. Qualitative and quantitative approaches to conceptualization and measurement involves familiar tensions between deep attention to texts versus breadth and representation across a broader collection; between human comprehensibility and optimal work with complex, multidimensional mathematical and linguistic representations; and between familiarity and precision. Good research designs, like those discussed in this chapter, mitigate these tensions and emphasize the strengths of both methods.

How can regression-type studies, which try to get causal inferences by controlling for variables until whatever is left is plausibly a causal relationship, benefit as much as possible from qualitative research? This chapter offers three qualitative research designs that can help test and improve quantitative causal inferences based on control variables. First, we consider process-tracing designs that seek to discover previously unknown confounding variables that can be added as new control variables, thereby refining and improving the statistical model. Second, we discuss process tracing that seeks to confirm that control variables have a direct connection to the outcome variable, and therefore are relevant controls. Third, we discuss process tracing to check whether control variables might be posttreatment and therefore at least partially harmful. Finally, we provide a demonstration of the kind of iterated interaction that we have in mind between statistical efforts at causal inference and qualitative research to learn more about the assumptions behind the model, involving the relationship between labor union strength and the prevalence of employment law across U.S. states.

How can mixed-method research add value to experiments? There is much to learn by adding qualitative components to experiments that can test for problems related to measurement; networks and spillover among experimental subjects; issues related to the meaning of treatments, outcomes, and other aspects of the experiment for participants; the exhaustiveness of the intended causal content of the treatment; and moderation effects related to subgroups of participants, or to differences across implementations of an experiment in divergent contexts. Some of these areas relate to the goal of theoretical generalization from the initial experiment, but most of them also have connections with the meaning and quality of the causal inference for the experiment at hand.

This chapter summarizes key themes from the book as a whole and offers general concluding thoughts.

Process tracing methods, which involve ways of using information within a single case to think carefully about questions of social-science theory, have become one of the leading qualitative methods in political science and related fields. Can mixed-method approach to process tracing work without destroying the qualitative logic of the method? This chapter argues that two distinct forms of process tracing, involving testing of existing causal theories and discovery of new theories, each have room for mixed-method strategies. We show that qualitative components can become steps within a testing-based process tracing argument, and offer guidelines for structuring such studies. We then offer a novel approach to combining machine-learning techniques like LASSO regression with discovery-based process tracing, illustrating the design with an application to the rise in visibility of U.S. far right broadcaster Alex Jones.

How can quantitative methods support case studies in general? One common task in case-study research is presenting audiences with a large collection of texts. Text-as-data methods may facilitate this task by summarizing the overall collection more quickly and understandably than we can usually do ourselves, and by locating individual documents within the collection. We offer an application, showing how text-as-data methods effectively summarize the large collection of legal documents available regarding defendants in cases related to the January 6th invasion and facilitate case-study exploration and understanding of specific individuals. We also discuss how mixed-method approaches may save time and resources as a way of moving across levels of analysis within a case-study design. We wind up the chapter by raising a note of caution regarding common attempts to use statistics as a test of generalizability for case studies and regarding the somewhat less common but still recurrent designs that mix game theory and qualitative research. Both of these approaches can work, and can offer insights, but both are harder than they may initially appear.

Research designs can combine qualitative, quantitative, experimental, and data-science methods through careful planning, attention to details and assumptions, and methodological execution. This chapter introduces the book's perspective, arguing that designs that combine methods carefully and with close attention to their details will outperform designs where two or more methods are carried out in parallel and then compared. The chapter also considers a range of practical and philosophical questions related to whether it makes sense to combine methods. Is one method always best, such that it isn't worth trying? Do methods speak fundamentally different languages, such that there is no point? Are methods and problems inextricably linked, such that the question is irrelevant? We argue against each of these views.

This chapter explores the intersection of political science and human rights, highlighting the discipline’s critical role in shaping human rights education, policy, and enforcement. Political scientists have significantly contributed to the conceptualization of human rights, analyzing whether they are universal or culturally relative and examining their philosophical and empirical foundations. Additionally, the chapter discusses how political scientists examine the role of political institutions, including international courts, foreign policy frameworks, and human rights enforcement mechanisms, in shaping global human rights norms. The chapter also explores how political science methodologies – ranging from qualitative analyses to large-scale data collection – have influenced human rights measurement and advocacy. The chapter further addresses contemporary challenges, such as the impact of populism on human rights protections and the role of interdisciplinary approaches in advancing the field. Ultimately, this work underscores how political science scholarship continues to evolve, shaping human rights discourse and policy implementation in an increasingly complex global landscape.

Political scientists have discovered the European Court of Justice, but has it discovered law? We address this enduring question, first posed by Armstrong in 1998, by tracing the evolution of law in political science work on the CJEU, from a concept understood in rudimentary terms as an external constraint on judicial behaviour to more recent nuanced accounts of legal concepts, doctrine, and judicial practices. While political science has come closer to the nuts and bolts of CJEU decision-making, we argue that there is untapped potential in exploring the multidimensionality of legal cases and the micro-level details of legal interpretation and adjudication.