The public discourse on class and stratification in Japan experienced a dramatic paradigm shift towards the end of the twentieth century. Although widely portrayed as an egalitarian and predominantly middle-class society during the period of high economic growth until the early 1990s, Japan was suddenly deemed a society divided along class lines under the prolonged stagnation that characterized the Japanese economy for three decades, from the 1990s to the 2020s.

Based on macro-sociological data, this chapter delineates the focal points of debate on the analysis of class and stratification in Japan as a general prelude to specific spheres covered later in the book – cultural diversity and class competition in relation to generation, region, labor, education, gender, ethnicity, and so on.

This chapter begins by introducing selection – the appointment, promotion and reassignment of international officials – as an administrative decision exercising discretionary authority, that is inherent to securing the highest standards of efficiency, competence, and integrity necessitated by the treaty-basis of international organizations. Second, the legal principles of selection are identified, namely: (1) Absent abuse of discretion, deference is due selecting managers; (2) Vacancy announcements must be adhered to, but may be revoked; and (3) Selection defects must be manifest, to be contestable. Third, two attendant legal duties of selection are considered, as follows: (1) Selection must be competency-based; and (2) All candidates are entitled to consideration, but not selection. Fourth, non-competitive selection – through direct selection and reassignment – is examined. Fifth, the classification of positions that underpins the objectivity of selection is considered. Sixth, and in conclusion, the employment law of selection at international organizations is restated.

Work on causation in psychopathology often emphasizes variation in the causes, but variation in what is to be explained further complicates matters. Focusing on the protean nature of psychopathology, this chapter explores different ways classificatory variation is generated. For example, choices about what features of disorders to foreground and background can produce variation. The chapter also examines, from the perspective of scientific conventionalism, how classificatory decisions made at choice points partly constitute what is classified, but not in the sense of making it up. In contrast to the view that conventions are neither true nor false and thus isolated from the domain of facts, the chapter argues that scientific conventions are implemented to promote the discovery of facts. Scientific conventions must also answer to conceptual and factual constraints. The chapter concludes by looking at how classificatory choices can produce different versions of a psychiatric, which may also result in variations in causal models across those versions. In agreement with the ideas articulated by Putnam, the chapter argues that we cannot divide the language of psychopathology into a part that describes disorders as they are in themselves and a part that contains our conceptual contributions to what we know about disorders.

Descriptive psychiatry has served our field well for more than half a century. The need to rely on phenomenology cannot be blamed for the structural errors built into the DSM system, such as choosing categories over dimensions, pseudo-precision to serve interrater reliability, and the arbitrary division of psychopathology into nearly three hundred distinct categories. Like all of medicine, psychiatric treatments exhibit unacceptable levels of variability in patient outcomes, consistent with cryptic mechanistic heterogeneity underlying indistinguishable clinical presentations. The result is an aspiration for precision medicine using biomarkers to select the right treatment for the right patient at the right time. Unlike most of medicine, however, psychiatric disorders lack clear natural anchors, like amyloid and tau in Alzheimer’s disease, that nominate mechanisms, biomarkers, and treatment targets. Hyman argues that it is now scientifically possible, albeit still very difficult, to begin identifying mechanisms and biomarkers, and that a concerted effort is warranted.

This chapter uses the Romantic novel as a case study to consider the problem of genre in world literature studies. Examining different frameworks for thinking about the category of Romanticism in a global context, it suggests that the historical novel provides a clear illustration of how the boundaries of genre expand and contract in order to conform to reigning paradigms of literary history. After demonstrating how Sophia Lee’s The Recess problematizes the idea of historical fiction that it is said to inaugurate, the chapter briefly compares it to Heinrich von Kleist’s Michael Kohlhaas and Alexander Pushkin’s The Captain’s Daughter in order to illuminate the criteria that come into play when positing whether these three texts should be considered as being of the same genre.

Languages can develop different dialects in different regions. Then, as further changes accumulate, speakers of the different dialects may no longer be able to understand one another. When that happens, these formerly different dialects become distinct but related languages, members of a language family. Linguistic classification is about these relationships among languages. This chapter is about the world’s language families, and about how languages are classified. It delves into the questions of what it takes to show that languages are related to one another and belong to the same language family. The major language families of the world are introduced. The comparative method is introduced and illustrated, and its huge role in explaining language history is revealed. Some of the better-known but nevertheless controversial proposals of distant linguistic kinship are also evaluated.

This chapter is devoted to the recently defined concept of strongly regular signed graphs (SRSGs), which generalizes the notion of strongly regular graphs from the domain of ordinary graphs. It presents the basic properties of SRSGs, provides specific constructions, and, as a fundamental result, classifies them into five disjoint classes. Each class is examined in detail, highlighting both similarities and distinctions with strongly regular graphs. The chapter also explores particular connections with 3-class association schemes, as well as constructions of signed Johnson and signed Hamming graphs. The concept of strong regularity is further combined with net regularity and walk regularity, with particular attention given to signed graphs that belong to the intersection of these classes. SRSGs with a small number of eigenvalues are analysed, accompanied by numerous illustrative constructions. For example, it has been shown that every connected signed graph with exactly two eigenvalues must be strongly regular.

When the outcome Y is discrete rather than continuous, we refer to the problem of predicting Y as classification. In many ways, this is easier than predicting a continuous outcome since Y can only take a few values. Most of the methods we have covered so far can be adapted to handle discrete outcomes. One particular method, based on neural nets, is covered in Chapter 12.

This chapter explores the application of large language models (LLMs) in empirical legal studies, with a focus on their potential to advance research on EU law at scale. The chapter provides a non-technical introduction to LLMs and the role they can play in legal information retrieval, including the classification of case characteristics and outcomes, which constitutes one of the most common research tasks in legal scholarship. The chapter stresses the importance of validation – researchers cannot treat the output of LLMs as automatically correct and instead must demonstrate the relevance and reliability of measures and results obtained through the use of LLMs in the context of their research topic. While LLMs are capable of significantly reducing the cost of doing legal research, their use will place growing demands on scholars to ensure the integrity of their findings. The chapter also reflects on the distinction between closed- and open-source models and how ethical and replicability imperatives might influence model choices in an increasingly crowded field.

Are people already at increased risk for disease more likely to be exposed to the risk factor of interest? Does closer observation of people with a disease lead to a false association? In retrospective studies, do people with a disease recall prior exposures more (or less) that healthier people? Are research interviewers a source of biased data collection? Confounding is an existential threat in biomedical research; here a second factor, which is associated with both the disease and the risk factor being studied, is an actual cause of the disease. If studies cannot fully control for the effect of the second risk factor, residual confounding will bias the risk estimate. Who participates and doesn’t participate in research is another source of bias. How diseases and risk factors are classified and categorized may introduce bias, and changing defined categories is yet another source of bias.

Chapter 2 tells the story of how ethnicity came to be known in Kenya through territory, providing an overview of the history of ethnic territorial boundary drawing from its inception with the first colonial administration, to today. The principal motivation for the earliest hard boundaries between purportedly homogenous ethnic groups was to free up land for white settlement and capital accumulation. After independence, the administrative boundaries of provinces and districts were deliberately retained, and ethnic patterns of land settlement were engineered. With multi-party elections in the 1990s, these established ‘ethnic territories’ motivated electoral gerrymandering, the most significant postcolonial driver of ethnic territorialisation. All these practices cemented a profound connection between land, boundaries, identity, rights, power, and security. I show how the 2010 constitution worked within this paradigm, too, but in novel ways that moved toward vagueness to manage the inflammatory, grievance-based politics tethered to boundary drawing in Kenya. In doing so, I show how ethnic territorial population concentration today is less certain than commonly imagined.

This chapter presents a set of practical, classroom-tested exercises for teaching concept analysis, emphasizing how deliberate engagement with concepts improves research and communication. It outlines several strategies, including reconceptualizing familiar terms by identifying defining and elective attributes, and situating them within semantic fields. It highlights the heuristic power of Collier’s question, “What is that a case of?”, which prompts students to move from empirical examples to abstract categories. Taxonomy construction is another key tool, helping students systematize ideas across domains – from constitutions to cuisine – and understand how classification affects knowledge. Binary sorting (“There are two kinds of people…”) and genre-mapping (“What do you work on?”) also serve to stimulate reflection on research categories. The chapter argues for the pedagogical value of testing, suggesting that students benefit from identifying, defining, and illustrating core concepts as a way to internalize intellectual terrain. Field exams, concept glossaries, and vocabulary tests help solidify these connections. The chapter concludes with a case for “conceptualism” as a core scholarly orientation: Concepts allow generalization while grounding knowledge in empirical cases. Working with concepts is cognitively satisfying and essential for memory, communication, and cumulative learning – what more could a good course (or concept) hope to achieve?

This concluding chapter offers some final reflections on the nature of knowledge about ethnicity in Kenya. I argue that if the nature of this knowledge is purposefully vague and makes ethnic categories polyvalent, then the best way to protect against problematic uses of ethnic knowledge is vigilance. This is far less satisfying and reassuring than law or rights as a framework for governing the risks of diversity, but it is far more appropriate, and I briefly consider what this might look like. Finally, I look forward to the digitisation of Kenya’s population register and aspirations to establish a population knowledge architecture so sophisticated that it could render numerous registers interoperable and ultimately replace even the census. I reflect on the nature of ethnic classification in such an architecture and argue that it would lose all the qualities that have made it amenable to solidaristic and pluralistic purposes thus far, while amplifying all its dangers.

This chapter concludes the volume by reflecting on the ongoing value of concept analysis in the social sciences. It revisits the tension between hyperfactualism – obsessive attention to granular detail – and the necessary abstraction that enables generalization. Conceptualization, the authors argue, helps scholars not only communicate more clearly but also observe and describe phenomena more effectively. Far from being a distraction, conceptual work sharpens empirical inquiry. The chapter highlights the interplay between conceptualization and measurement, especially in validity assessment, and underscores how concepts represent and structure knowledge. Attention to concepts also facilitates integration and translation across time, space, and disciplines, as seen in such examples as the V-Dem project. Issues of conceptual boundedness, typologies, and traveling are revisited, drawing on contributions from cognitive linguistics and classic debates between lumpers and splitters. The authors also reflect on how digital tools and formal modeling offer new avenues for concept innovation. Finally, they affirm the importance of teaching concept analysis as a way to clarify students’ thinking, research design, and disciplinary communication. In sum, the chapter defends the overconscious scholar: one who sees in concepts not distraction, but a path toward cumulative, communicable, and intellectually satisfying scholarship.

This chapter introduces the supposed problem of ethnicity: that it undermines national cohesion, or is a colonial hangover with no appropriate place in political life. In contrast, I argue that ethnicity is neither inherently desirable nor undesirable; its political effects depend on how it is known and used, and our understanding of how it is known remains underdeveloped. I establish that there is no definitive list of Kenya’s ethnic groups, and we must stop taking for granted what we think we know about ethnicity. I offer the concept of cultivated vagueness – a widespread aversion to resolving the ambiguity of lists of Kenya’s ethnic groups – to understand how ethnic knowledge works and to contrast it with legibility and governmentality. Cultivated vagueness is the response from bureaucrats, civil society, citizens and the state to the conundrum that ethnic knowledge is both common sense and impossible to settle. It also explains how ethnic classifications serve both projects of division and of pluralism. I suggest that attention to the benefits of cultivated vagueness may facilitate the advancement of the latter over the former. The chapter outlines the book’s methodology and chapters.