An introduction of the four classical models: the voter model, the contact process, stochastic Ising models, and the exclusion process, complemented by some variations on the classical models such as the two-stage contact process or biased and rebellious voter models, as well as some other models such as systems of branching, coalescing, and annihilating random walks and an example of a kinetically constrained model. Some well-known major results are mentioned together with many open problems and topics that are from the mathematical side and still poorly understood, such as the critical exponents associated with continuous phase transition and (in the final section) periodic behavior.

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.

The concluding Appendix seeks to understand how the detailed results of the previous chapters fit in with the broader current mathematical landscape. We explain how the Ando lifting problem fits in as a particular instance of the Arveson C-star algebra dilation framework. Within the Arveson framework, we identify an additional distinctive feature, namely the identification of a companion contraction, as an additional piece of structure. We identify some remaining open problems concerning commuting contractive pairs, which should be a fertile area for future research.

Here we give a short Introduction to Sz.-Nagy–Foias classical theory and then briefly describe the main contribution chapterwise.

This chapter examines the decentralized autonomous organizations (DAOs), which rely primarily on sociotechnical infrastructures supplied by blockchain technology and consist substantially of combinations of shared computer code and shared data. The chapter considers DAOs using the governing knowledge commons (GKC) research framework, contrasting the GKC perspective with long-standing views of the corporate form as a nexus of contracts, as an instance of hierarchy and decision theory, and as a complex system. The analysis is set against the context of earlier work on the corporation as commons. The chapter concludes that the GKC framework focuses attention on elements of governance that often are not salient in conventional accounts. This is especially true of the important question of how governance responds to and generates social dilemmas associated specifically with practices of sharing knowledge, information, and data.

This chapter examines William Burroughs as a radical world builder within science fiction, exploring how his early and late works engage deeply with the genre’s tropes – time travel, space opera, interplanetary conflict, and dystopian urban control. From the Cut-Up/Nova trilogy to The Wild Boys and the Red Night trilogy, Burroughs constructs fragmented urban landscapes and hybrid futures that destabilize genre conventions and resist linear narrative. Situating Burroughs among proto-SF, Golden Age authors, and the New Wave of the 1960s, Hougue explores how his stylistic innovations – especially the cut-up method – helped redefine the speculative tradition. Clémentine Hougue traces Burroughs’ influence on writers like Samuel Delany, William Gibson, and Kathy Acker, showing how his mutating urban imaginaries prefigure cyberpunk and experimental SF. Moving from dystopian insurrections to utopian reimaginings of space and time, Burroughs’ work becomes a site of poetic and political speculation, operating at the boundaries of genre and form.