This chapter revisits the book’s central argument and conclusions from each chapter. It concludes that there has been substantial misunderstanding about core aspects of deterrence, which can be addressed by working from a comprehensive approach to theorizing deterrence and using this approach to guide and evaluate research. The chapter also concludes that most extant deterrence-based policies cannot and will not appreciably deter crime, and may even worsen it. The solution lies in policies grounded in stronger science built on better theory and research. Our sincere hope is that comprehensive deterrence theory (CDT) provides a helpful step in that direction.

This chapter discusses the centrality of deterrence to criminological theory and to policy, and then highlights critical shortcomings in classical deterrence theory. It points to critical problems that these shortcomings create, including incomplete or inaccurate understanding of deterrence and ineffective policy. The chapter then describes the motivation for the book, which is to advance theory and policy, the structure of the book, each of the chapters, and recommendations for sequences of chapters readers can follow to pursue their particular interests.

This chapter describes the origins of deterrence theory and problems with the overly narrow conceptualization of deterrence. It discusses the problems within the context of contemporary criminology and criminal justice policy. Many policies rest on weak or inaccurate understanding of deterrence, or are premised on research that has limited generalizability. One example: A great deal of criminal justice policy focuses only on punishment severity as a way of influencing deterrence, but one can increase deterrence in other ways, such as increasing the certainty of punishment or increasing the rewards of non-crime.

Prediction science is likely to push on toward distinct reconceptualizations or the dismantling of the cornerstones of traditional cognitive science, away from rule-based symbol manipulation and toward a comprehensive systems prediction science, toward theoretical unification and simplicity, toward figuring out the pros and cons of the representation-light and representation-heavy, toward incorporating analog representations and common codes, toward proactive, probabilistic, mechanistic, and formalized theories, and computationally specified models of the predictive mind. The paradigm shift of the predictive revolution is no longer only emerging: it is continuing at an ever-increasing pace.

This introductory chapter presents the aims of the book and its rationale in Section 1.1, including some insight into the author’s experience of teaching sign language for many years. Next, Section 1.2 provides some suggestions for how you can best use the book to advance your learning and provides explanation for some of the terminology and conventions used in the book. This section also provides some awareness of the systemic barriers faced by many sign language teachers and the limited amount of research on BSL teaching and learning. The following section, Section 1.3, then gives some details about the research and evidence basis of the book in order to provide the reader with some awareness of current research and understanding of this visual nature of the language. Lastly, Section 1.4 addresses a few questions that students commonly ask and expels some common myths around learning BSL.

Effective skills in evaluating information are now incredibly important in a world awash with misinformation and the new affordances and challenges of generative AI. Finding and using quality information to enhance your learning and evaluation capability is an important part of your lifelong learning journey. Your reputation as a professional hinges on being able to make informed decisions based on best practice and good research.

This chapter explores the professional information landscape, identifying various information source types and where you can access them. It identifies key challenges related to online access to information. It then explores the four stages of being information literate: identifying your information needs, then finding, evaluating and using information effectively. It outlines what it means to enter academic and professional conversations and how we acknowledge the ideas of others in our own work. The Extend your understanding section outlines the research process and describes the quantitative and qualitative approaches commonly used in communication research.

In this book, I examined how public authorities’ reliance on algorithmic regulation can affect the rule of law and erode its protective role. I conceptualised this threat as algorithmic rule by law and evaluated the EU legal framework’s safeguards to counter it. In this chapter, I summarise my findings, conclude that this threat is insufficiently addressed (Section 6.1) and provide a number of recommendations (Section 6.2). Finally, I offer some closing remarks (Section 6.3). Algorithmic regulation promises simplicity and a route to avoid the complex tensions of legal rules that are continuously open to multiple interpretations. Yet the same promise also threatens liberal democracy today, as illiberal and authoritarian tendencies seek to eliminate plurality in favour of simplicity. The threat of algorithmic rule by law is hence the same that also threatens liberal democracy: the elimination of normative tensions by essentialising a single view. The antidote is hence to accept not only the normative tensions that are inherent in law but also the tensions inherent in a pluralistic society. We should not essentialise the law’s interpretation, but embrace its normative complexity.

This chapter takes seriously the concerns of Eliot’s early reviewers with a tension in her fiction between the devoted depiction of life later associated with realism, and a didactic impulse to which they increasingly felt she succumbed. Asking why Eliot interrupted representation with theorisation, the chapter takes as a case study her alternating dramatisation and analysis of incongruous versions of history in Chapter 20 of Middlemarch. It traces the lineage of such alternation, via an allusion to her friend John Sibree’s translation of Hegel’s Lectures on the Philosophy of History, into one of the notebooks Eliot used as she developed Middlemarch, which is read less as a source for either the novel’s theories or its facts than as a laboratory for its experiments in moving between them. The chapter suggests that Eliot valued the dissonance her reviewers detected when dogma intruded upon depiction. It thereby elucidates her contribution to the dialectical novel of ideas this book explores.

This chapter provides an overview of foundational principles that guide CA research, offered both on the basis of our own experiences as researchers, and from our discussions with other conversation analysts as they authored contributions for the present volume. We begin by briefly sketching of some of the fundamentals of human social interaction, in order to underscore CA’s central focus, the study of social action, and describe some of the basic features of how interaction is procedurally organized. These basic features of interaction, which CA research has rigorously evidenced and which guide our examination of new data, are then shown directly to inform CA as a research methodology. Put another way, it is precisely due to the procedural infrastructure of action in interaction that conversation analysts use and work with interactional data in particular ways. We conclude with advice for readers as they continue to explore the volume’s contents.