This chapter starts by defining the kind of behavioral data to which methods in this book apply: discrete ordinal variables with a finite number of categories (which includes dichotomous variables). It continues by describing the behavioral variables in the Teenage Friends and Lifestyle Study, the data set used in many examples, and how they change over time. Typical issues of data coding, re-coding, and description are illustrated. Some important descriptive statistics are presented that connect the behavior data with the network, with the focus on network autocorrelation coefficients.

This chapter is on communication complexity, which measures the minimum number of bits that two parties must exchange to collaboratively compute a function when the input is split between the parties. Techniques for upper and lower bounds for deterministic and randomized communication complexities are covered. The chapter also presents applications of communication complexity to topics that don’t involve explicit communication, such as streaming algorithms, data structures, and integrated circuit layout.

This chapter uses the relationship(s) between data and mobility through physical spaces to explore why the characterization of data as infrastructure might matter for law- and policy-makers. As sets of structured data flows layered over other infrastructures have multiplied and intersected with practices of data extraction and appropriation, some of those data flows—in particular, data flows maintained by entities with the resources to manipulate them in real time and at scale—have come to function as infrastructures in their own right, destabilizing existing settlements regarding the movement of people and goods. Data infrastructures (are designed to) scale both vertically and laterally; they are nimble, flexible, and adaptable to new uses. The infrastructure lens helps to explain some of the challenges that regulatory efforts have faced and suggests some important questions about newer, platform-specific regulatory models that are now beginning to emerge.

Chapter 3 focuses on doing privacy by design for Clever Urban Computing systems, that is, interconnected physical sensors and AI-based data analytics embedded into buildings, streets, and wider-city infrastructures. Personal data is central to such systems, so accounting for privacy and data protection laws is critical. This chapter charts the emergence of Clever Urban Computing, from ‘smart cities’ towards AI urbanism. Examples show the problematic assumptions and problems underpinning many initiatives. ‘Smart buildings’ are analysed to foreground the longitudinal and infrastructural aspects of designing for privacy. Privacy by design and ‘data protection by design and default’ are analysed at length. Legal analysis concentrates on privacy as a value, alongside the EU General Data Protection Regulation (GDPR) and the EU Data Act 2023. New HCI-led approaches are proposed as examples of Accountable Design. This includes designing for consent through ‘consent trajectories’, developing a ‘privacy by participatory design’ approach, and user-centred data management. Empirical data from an expert interview presents why privacy is hard to design for.

Chapter 1 provides an overall introduction to the book and its underlying motivations. It proposes and defines the concept of Clever Computing. It presents the historical emergence of the Internet of Things, AI, and Ubiquitous Computing, and analyses the risks of technological visions of the future which often neglect current social and human needs in favour of techno-led solutions. It explores the need to account for human needs in design and, by extension, legal values. The term Clever Computing is contextualised by exploring different examples of how AI and IoT are increasingly converging. This focuses on the ways AI is changing and improving interaction techniques with IoT; how AI is being used to make sense of IoT data and enabling distributed data analysis; and how AI is augmenting the existing functionality and increasing the autonomy of IoT systems.

This chapter looks at the global picture of migration management and aid. We present the first-ever global estimate of migration management aid from 2002 to 2022. Based on the Organisation for Economic Co-operation and Development (OECD) data on official development aid, we find that states of the Global North spent more than $73 billion on development aid to manage migration within developing countries, increasing from $718 million in 2002 to $8.71 billion in 2022. The United States and EU donated similar levels of migration management aid before 2015, but after 2016 the EU contributed more than 50 percent of all migration management aid every year – reflecting the importance of the EUTF. The top recipients of migration aid were Turkey, Iraq, Syria, the West Bank and Gaza, and Lebanon, which all represent key hosting states for refugees during this twenty-year period. This chapter shows how migration management aid has emerged as a global trend, despite variation in implementation, purpose, and region.

This chapter introduces key assessment principles and practical strategies. You will explore ways to foster evaluative judgement and reflective thinking, design rubrics, and use diagnostic, formative, and summative assessments to gather insights. The chapter also covers inclusive practices, authentic tasks, and the role of data in driving improvement. These strategies are examples to adapt to your own students and teaching context, helping you create a responsive, engaging learning environment that promotes growth for all learners.

Chapter 1 introduces ideology, and Guidelines, Theses, Procedures, Caveats for empirical ideological research in pragmatics and discourse analysis presented in Jef Verschueren’s Ideology in Language Use: Pragmatic Guidelines for Empirical Research. It introduces the data to be analyzed: Amnesty International reports, webpages, emails, appeal letters addressed to powerful individuals on behalf of Prisoners of Conscience, and, for contrast, two racist American Nazi documents. It briefly introduces the main foci of the analysis: the documents’ intended audience, language, temporal and geographic perspectives, and the coverage and degree of detail that the documents provide regarding the situations they describe. As many people, including Amnesty International, deny that liberational discourses such as that of human rights are ideological, the book asks whether the AI documents are in fact ideological. The remainder of the book demonstrates that they fulfill the criteria defining ideological discourse proposed in Terry Eagleton’s Ideology: An Introduction and by Verschueren and are therefore undeniably ideological. The chapter concludes with a brief plan of the following chapters.

Chapter 15 explores the foundational practices of planning, assessing, and using data in mathematics education. It unpacks the essential components of effective mathematics lessons and outlines strategies for designing learning sequences that are responsive to student needs. The chapter introduces formative and summative assessment practices, the role of feedback, and how student learning data can be used to inform, adapt, and improve teaching and learning.

Chapter 12 extends students’ understanding of Statistics and introduces foundational concepts in Probability for Years 3 to 6. You will explore how to support students in collecting, organising, and interpreting data, identifying patterns, and predicting outcomes using simple probability language. The chapter also highlights strategies for integrating digital tools, adapting tasks to meet diverse learning needs, and making cross-curricular connections to enhance relevance and engagement.

Chapter 11 introduces students’ early engagement with Statistics in the Foundation to Year 2 level. It focuses on key concepts such as posing questions, collecting data, and interpreting simple visual representations. You will explore essential language, sample activities, and assessment strategies, along with common misunderstandings to look for when supporting young learners in developing foundational data skills.

The exploration proposed here is pursued through a complex, regional case study. Regional case studies enable delineating a portion of the world, with a consistent set of institutions and policies as well as geographical and material conditions that set the frame for people’s lives, and to identify the complex dynamics by which sociogenetic, microgenetic and ontogenetic transformation co-occur. This chapter presents how we approached, conceived and analysed this case study. To start with, I define my approach to ageing as a form of personal engagement, which progressively developed into a collaborative project. After showing the relevance of a regional case study for sociocultural psychology of the lifecourse, I present the fieldwork, the data collection, an overview of the participants and the main line of the analysis.

This chapter analyses how trade law conceptualises data and AI. It shows that trade law applies long-established concepts to these novel phenomena while experimenting with new categories in preferential agreements. For data, these categories include data as a good, as a service, as a digital product, intellectual property, electronic transmissions, and as a regulatory object. For AI, the chapter distinguishes between the trade regulation of AI components, AI products, and AI governance. It concludes by suggesting that trade law can be understood as a form of AI/data law, which may help in recognising and addressing the challenges that the digital economy poses for trade law.

Chapter 3 offers an initial empirical assessment of the book’s main argument. It begins by identifying the partisan Left and Right in postcommunist Europe and analyzing patterns of their programmatic positions and ballot box results. The findings support the theoretical expectation that the long-term economic stances and electoral performance of the Left, but not of the Right, are two mutually related legacies of postcommunist junctures. Next, having developed a set of variables to compare long- and short-term electoral effects on the Left, I show that the former are the stronger predictor of illiberal electoral outcomes. The chapter closes with a discussion of why rival arguments prioritizing economic, political, and cultural demand, institutional and leadership supply, and international factors fail to adequately explain the variation identified in book’s opening chapter. This point is empirically reinforced in Appendix D, where I test the plausibility of the postcommunist juncture theory vis-à-vis rival hypotheses by using an original dataset and standard statistical methods for cross-sectional and time series data. The results show that the long-term regularities rooted in postcommunist junctures explain illiberal electoral outcomes in the most significant and consistent way.

This chapter offers an overview of the varieties of data that are used in EU law scholarship alongside an overview of the associate research methods employed to analyse it. Based on a systematic literature review of 248 academic articles in the area of EU law and EU courts specifically, it addresses two questions: first, what data sources and methods are the most prevalent in EU law? Second, what are the advantages and pitfalls of different data sources and research methods and how can an understanding of these improve the study of EU law? Finally, the chapter seeks to stimulate a critical discussion of the extent to which emerging and non-traditional data sources both complement and challenge the traditional understandings of what counts as law. The chapter starts with an overview of the most commonly used source of data in EU legal research on courts – courts’ case law – before turning to other, less traditional sources of data in EU law such as interview and survey data, and data based on official statistics, newspapers, and courts’ websites.

The chapter discusses the creation and maintenance of databases offering accurate, research-ready data for multidisciplinary use. It draws on the experience with the IUROPA CJEU Database Project (IUROPA), which has collected data about the decision-makers and the decisions of the Court of Justice of the European Union (CJEU). IUROPA and similar multi-user databases must live up to four criteria for databases, as proposed by Weinshall and Epstein. First, they must address real-world problems. Second, they must be open and accessible. Third, they must deliver reliable and reproducible data. Fourth, they must be ageless and easily calibrated to research purposes unknown at the time of data collection and cleaning. These criteria involve trade-offs: the quest for reliability may, first, precipitate difficult choices such as whether to discard or improve upon ‘imperfect’ data or tempt creators to endlessly postpone publication of ‘incomplete’ data; second, sustainability and human intervention are inversely proportionate when it comes to database maintenance; finally, a fledgling discipline like empirical legal studies in EU law imposes a disproportionate time commitment and financial responsibility on a small group of researchers.