This chapter examines the relationship between human rights and Chile’s 1990-1991 National Commission on Truth and Reconciliation, and the way this relationship continues to shape state-society relations in the aftermath of Pinochet’s dictatorship (1973-1990). The argument is two-fold. First, the Truth Commission draws on the language of human rights to authorize its account as the ‘major’ truth. Second, in doing so, the Truth Commission displaces from public life ‘minor’ truths, specifically the experiences of state-violence by Indigenous communities (Mapuche people) and women (Arpilleristas). The argument is based on an analysis of the representation of truth and authority embodied in Chile’s Museum of Memory and Human Rights. The chapter shows how the Museum gives continuity to the work of the Truth Commission by giving it a material (spatial and experiential) form. While the Truth Commission and the Museum remain two distinct institutions, in visiting the Museo/Truth Commission it becomes apparent how human rights authorizes the Truth Commission’s account, and how the Museo gives it continuity in public life.

This chapter will provide an overview of quantitative designs in corpus linguistics. Section 3.1 introduces the kinds of research design questions nearly every quantitative corpus linguistic study must involve at the planning stage: (1) what corpus linguistic statistic(s) to use and (2) how to evaluate them to inform the conclusions of a study. Section 3.2 is devoted to statistical methods that are, in a sense, ‘specific’ to corpus linguistic applications beginning with different kinds of frequencies, entropies, and keyness values, before turning to co-occurrence phenomena and its association measures as well as dispersion measures. Section 3.3 is concerned with ‘general’ statistical methods. It begins with a short mention of monofactorial statistics (e.g., chi-squared tests or correlation coefficients) before turning to multifactorial statistics, in particular fixed- and mixed-effects regression models, their extensions and combination, and increasingly prominent tools such as structural equation modeling or tree-based methods. This is followed by a brief discussion of exploratory methods such as multidimensional analysis (MDA) and approaches like cluster or correspondence analysis. I conclude with a few words of caution and desiderata regarding what practitioners need to bear in mind as they gravitate to the more complex methods our field often requires.

Representativeness is a critical consideration in corpus linguistics as it ensures that the linguistic analyses conducted on a corpus can yield valid and generalizable insights about the target domain. Without adequate consideration for representativeness, findings may be skewed, thereby undermining the reliability of any conclusions drawn. Despite its importance, many corpus-based studies neglect planning for and evaluation of representativeness, which poses limitations to the accuracy and applicability of their results. Although corpus size has been recognized as a determinant of representativeness, domain analysis is an equally important element that has not received as much attention. This chapter utilizes the model of corpus representativeness proposed by Egbert, Biber, and Gray (2022), which advocates for a detailed approach to both domain analysis and sample planning. The model, which is rooted in statistical and logical rigor, underscores the importance of minimizing coverage and selection biases. The chapter applies this model in a case study on constructing a corpus of AI-simulated human conversations. By following the model, the case study illustrates the processes of domain specification, operationalization, and sampling to achieve a high level of representativeness for the corpus.

The Governing Knowledge Commons (GKC) framework draws attention to the content, quality, and consequences of the production, the institutionalized (community) governance, and the sharing of knowledge. In the domain of corporate governance, the key knowledge in question concerns the rules, mechanisms, and infrastructures that enable corporations to be governed. But how do actors understand what is going on and what is at stake in the field of corporate governance? Drawing on the sociological theory of Strategic Action Fields (SAF), this chapter provides an account of how different imaginaries of corporate status, architecture, governance, and purpose are actively created and promoted by different kinds of disciplinary specialists, standard setters, and practitioners. The chapter shows how the knowledge claims made by these epistemic communities up the 1960s and from the 1970s onwards underpin two competing social norms of corporate governance, which were expressed in different configurations of position, boundary, choice, aggregation, information, payoff, and scope rules.

This chapter provides an overview of the tools and methods used in corpus linguistics, with a focus on their applications in both research and educational settings. It first examines the range of ready-built online and offline tools available to researchers, teachers, and learners, comparing these to do-it-yourself (DIY) tools that can be developed using programming languages such as Python or R. Next, the chapter explores the role of corpus tools at various stages of a research study, including corpus compilation, cleaning, tagging, annotation, and analysis. It then provides a detailed discussion of how tools and methods can be used to analyze language at both the ‘bottom-up’ (e.g., word, phrase, sentence) and ‘top-down’ (e.g., paragraph, section, discourse) levels, introducing analytical methods, such as key-word-in-context (KWIC) concordances, concordance plots, clusters, n-grams/lexical bundles, collocates, word frequencies, and keywords. Finally, the chapter explores recent advancements in artificial intelligence (AI), particularly the emergence of large language models (LLMs) and their potential impact on corpus linguistics. These technologies have the potential to enhance traditional corpus tools and methods while opening new avenues for corpus-based research, teaching, and learning.

This chapter shows how all the concepts introduced earlier can be put to use. It does so by introducing the concept of log-linearization and, in a second step, using the method of undetermined coefficients, which allows model variables (“control variables”) to be put as functions of a group of “state variables.” While doing this process by hand is cumbersome, computer software -- in particular, Dynare -- can easily handle it.

This chapter illustrates how to add stochastic shocks to an economy; if it helps, it explains the “S” part of “DSGE.” In particular, the chapter models shocks as Markov processes, for which the dynamics of the shock depend only on the value that the shock takes today.

Gabriele Magni examines the experiences of LGBTQ+ women running for office at various levels. Tracing the historical evolution of these candidacies from the 1970s all the way to 2024, the analysis shows how the number of LGBTQ+ women running for office has increased over time and how the group has grown more diverse along gender identity, race, and ethnicity. The chapter then explores the challenges that LGBTQ+ women face when running for office, highlighting both similarities and differences with straight, cisgender women as well as male candidates. Subgroup analysis then reveals how transgender women and LGBTQ+ women of color face heightened obstacles. The analysis also shows that, despite the challenges, cisgender lesbian women often perform at least as well as their straight, cisgender counterparts in elections. The chapter concludes with an assessment of the factors that can help increase and improve the political representation of LGBTQ+ women.

Chapter 7 reviews the main findings of the analyses presented in Chapters 1-6. These identify essential characteristics of ideology and demonstrate that the appeals exhibit these characteristics and that the methods typically used for the analysis of nefarious ideologies can be profitably applied to liberational ones. They augment Verschueren’s analytic procedures with technical and theoretical devices to account for the interplay among implicit and explicit meanings, indices of discourse interaction, and metapragmatic activities. It reviews the claim that the appeals are designed for at least two audiences: their addressees and Amnesty members. It revisits discussion of ideology in light of the analyses of the Amnesty documents, contrasting these and other human rights documents with mythic and ritualistic elements of two US Nazi documents. The chapter concludes by discussing unexpected findings, changes in human rights, Amnesty’s adaptations to changes in communication technologies, reasons for counterscreening and an invitation to use the additional corpus of appeals in Appendix 3 for this purpose, and suggestions for further research on oppositional activist discourses.

Scholars of war and society should consider Native nations not simply as the sorry targets of U.S. conquest, but as sovereign, war-making societies themselves. This essay reconsiders the era of the U.S.-Mexican War as a case study in how Indigenous war-making affected state-organized societies in North America. Beginning in the 1830s, Comanches, Kiowas, Apaches, and Navajos abandoned fragile peace agreements with Mexico and launched raids across ten states. By the mid-1840s, raids and counterraids had killed thousands, wrecked local economies, depopulated rural areas, and fractured Mexican political unity. When the U.S. army invaded in 1846, northern Mexico—exhausted and divided after 15 years of Indigenous warfare—was unable to effectively resist.These conflicts also conditioned how Anglo-Americans and Mexicans perceived each other. Neither acknowledged Native nations as strategic actors, blaming one another for Indigenous violence instead. Ultimately, U.S. officials portrayed the dismemberment of Mexico as an act of salvation. Native war-making shaped 19th-century North American geopolitics in enduring ways and it should be integral to the study of war and society in America.