This chapter explains how ubiguitous child sexual abuse really is, and outlines its consequences. It is intended to provide a context for the story, over four years, of one of the most successful hunting teams in Britain. As such, it is rich in numerical data.

This conversation addresses the impact of artificial intelligence and sustainability aspects on corporate governance. The speakers explore how technological innovation and sustainability concerns will change the way companies and financial institutions are managed, controlled and regulated. By way of background, the discussion considers the past and recent history of crises, including financial crises and the more recent COVID-19 pandemic. Particular attention is given to the field of auditing, investigating the changing role of internal and external audits. This includes a discussion of the role of regulatory authorities and how their practices will be affected by technological change. Further attention is given to artificial intelligence in the context of businesses and company law. As regards digital transformation, five issues are reflected, namely data, decentralisation, diversification, democratisation and disruption.

This conversation centres around innovation in the financial services sector and the related regulatory supervision. Three ‘Techs’ are especially relevant: FinTech, RegTech and SupTech. ‘FinTech’ combines the words ‘financial’ and ‘technology’ and refers to technological innovation in the delivery of financial services and products. ‘RegTech’ joins ‘regulatory’ and ‘technology’ and describes the use of technology by businesses to manage and comply with regulatory requirements. ‘SupTech’, finally, unites the words ‘supervisory’ and ‘technology’ to refer to the use of technology by supervisory authorities such as financial services authorities to perform their functions. Particular approaches presented in this session include regulatory sandboxes to promote innovative technology in the financial sector, automated data analysis, the collection and analysis of granular data, digital forensics and internet monitoring systems. The speakers also address collaboration between financial institutions and supervisory authorities, for example, in the creation of data collection formats and data sharing.

This chapter establishes what it means to do discourse analysis. This is done by defining discourse analysis and providing examples of discourse. The chapter offers a practical overview of how the discourse in discourse analysis fits within the research process. The examples of discourse that are introduced in this chapter are grammar, actions and practices, identities, places and spaces, stories, ideologies, and social structures. After reading the chapter, readers will know what discourse analysis is; understand that there are many types of discourse; know that discourse is an object of study; and understand how an object of study fits within a research project.

Chapter 5 addresses a major demographic puzzle concerning thousands of New York slaves who seem to have gone missing in the transition from slavery to freedom, and the chapter questions how and if slaves were sold South. The keys to solving this puzzle include estimates of common death rates, census undercounting, changing gender ratios in the New York black population, and, most importantly, a proper interpretation of the 1799 emancipation law and its effects on how the children of slaves were counted in the census. Given an extensive analysis of census data, with various demographic techniques for understanding how populations change over time, I conclude that a large number of New York slaves (between 1,000 and 5,000) were sold South, but not likely as many as some previous historians have suggested. A disproportionate number of these sold slaves came from Long Island and Manhattan.

The chapter demonstrates that selecting an object of study is a consequential part of doing discourse analysis. Selecting an object of study requires considering many planning and analytic issues that are often neglected in introductory books on discourse analysis. This chapter reviews many of these planning and analytic issues, including how to organize and present data. After reading the chapter, readers will know how to structure an analysis; understand what data excerpts are and how to introduce them in an analysis; be able to create and present an object of study as smaller data excerpts; and know how to sequence an analysis.

This chapter seeks to achieve two main objectives. First, it revisits some of the broad theoretical principles first raised in Chapter 1 and discusses how these might influence the decisions made by stylisticians planning their research projects. Second, the chapter reviews the main methodological decisions which need to be made in planning stylistics projects, irrespective of the specific theoretical approach being used. The chapter aims to provide both the experienced and novice researcher in this field with a means by which to locate their own work in the context of stylistics as a whole. In so doing, the chapter considers both qualitative and quantitative approaches to text analysis. Concerning the latter, the chapter will outline particularly the advances made in recent years in what has come to be called corpus stylistics. Following a discussion of the theoretical and methodological aspects of research in stylistics, the chapter draws some general conclusions about the nature of stylistic research and how to navigate the field as a researcher.

Chapter 3 establishes that the Dutch had economic incentives to continue holding slaves. Slavery in Dutch New York was not just a cultural choice, but was reinforced by economic considerations. From archival sources and published secondary sources, I have compiled a unique dataset of prices for over 3,350 slaves bought, sold, assessed for value, or advertised for sale in New York and New Jersey. This data has been coded by sex, age, county, price, and type of record, among other categories. It is as far as I know the only slave price database for slaves in the Northern states yet assembled. Regression analysis allows us to compute the average price of Northern slaves over time, the relative price difference between male and female slaves, the price trend relative to known prices in the American South, and other variables such as the price differential between New York City slaves and slaves in other counties in the state. Slave prices in New York and New Jersey appear relatively stable over time, but declined in the nineteenth century. The analysis shows that slaveholders in Dutch New York were motivated by profit, and they sought strength and youth in purchasing slaves.

This book describes and explains the discipline of stylistics, the linguistic study of style in language. The authors’ account of stylistics roughly follows its development over time, beginning with its origins in the Russian formalist work of the early twentieth century and ending with the current state of the art as informed by recent research in cognitive and corpus stylistics. The authors’ aim in presenting this account is to establish anew the importance, coherence and achievements of stylistics and to argue for its status as a subdiscipline of linguistics. This opening chapter outlines in general terms what the label stylistics refers to, before going on to explain the necessary steps involved in the linguistic study of style and the main theoretical principles of the field.

This chapter introduces what a time series is and defines the important decomposition into trend, seasonal, and cycle that guides our thinking. We introduce a number of datasets used in the book and plot them to show their key features in terms of these components.

This chapter explores the knowledge creation aspect of contemporary tax reforms in Nigeria. It offers a historical perspective on this process which lets us see today’s reforms not only as the re-creation of long-retreated systems of state taxation-led ordering, but against the backdrop of what intervened in the meantime – a four-decade late-twentieth-century interregnum where revenue reliance on oil profits created a very different distributive system of government-as-knowledge. Today’s system of tax-and-knowledge is not just reform but an inversion of what came before.

This chapter introduces the main research themes of this book, which explores two current global developments. The first concerns the increased use of algorithmic systems by public authorities in a way that raises significant ethical and legal challenges. The second concerns the erosion of the rule of law and the rise of authoritarian and illiberal tendencies in liberal democracies, including in Europe. While each of these developments is worrying as such, in this book, I argue that the combination of their harms is currently underexamined. By analysing how the former development might reinforce the latter, this book seeks to provide a better understanding of how algorithmic regulation can erode the rule of law and lead to algorithmic rule by law instead. It also evaluates the current EU legal framework which is inadequate to counter this threat, and identifies new pathways forward.

The risks emanating from algorithmic rule by law lie at the intersection of two regulatory domains: regulation pertaining to the rule of law’s protection (the EU’s rule of law agenda), and regulation pertaining to the protection of individuals against the risks of algorithmic systems (the EU’s digital agenda). Each of these domains consists of a broad range of legislation, including not only primary and secondary EU law, but also soft law. In what follows, I confine my investigation to those areas of legislation that are most relevant for the identified concerns. After addressing the EU’s competences to take legal action in this field (Section 5.1), I respectively examine safeguards provided by regulation pertaining to the rule of law (Section 5.2), to personal data (Section 5.3) and to algorithmic systems (Section 5.4), before concluding (Section 5.5).