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.

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.

We provide a practical guide to applied time series analysis using ordinary least squares. In our opening chapter we discuss the ways in which time series data are different than cross-sectional data and then introduce the statistical consequences for those differences. We provide guidelines for assembling a time series dataset – including discussions of sampling windows, sampling intervals, data length, and operationalizing variables. We then provide a brief outline of the remainder of the book.

We are living in a time when many teachers say they are feeling burnt out, and many others have left the profession altogether. Even new teachers who might start out feeling enthusiastic are likely to leave the profession after a few years. Teachers say the pressures they feel don’t match their view of what teaching is supposed to be all about – caring for, and teaching, children and young people. So, what do teachers do? What does the public (and, for that matter, Hollywood movie producers) think teachers do? This chapter argues that we have a bit of a mismatch between what people outside the profession think, and the experiences of teachers themselves. It also argues that broader changes in education, such as the use of data to govern teachers’ work has created extra pressure on teachers.

There are all sorts of dilemmas when it comes to technology and education. How much should be allowed in schools? Do teachers have to worry about students’ data security and privacy? Is it ok for you to ask a computer to write your essay for you? Are we ruining the eyesight and attention spans of an entire generation thanks to excessive screen time? This chapter looks at the debates that exist when it comes to digital technology and education. It will be argued here that the interplay between technology and education is highly complex – and changing – at a pace that is almost unimaginable.

As business transactions and the global economy become increasingly digitalized, international investment disputes will deal with novel assets in new boundary-defiant contexts. Indeed, jurisdictional arguments and objections will likely require arbitral tribunals to confront with the uneasy task of delineating the ‘localization’ of investments in digital economy assets such as cryptocurrency, non-fungible tokens, and data-related investments. However, given that even more traditional assets have raised a variety of problems relating to territorial nexus and localization, the authors believe that the digital economy emphasizes what are essentially differences in degree rather than in kind. This chapter discusses the complexities that arise in considering the idiosyncrasies of investments in digital economy assets within a traditional territorially defined jurisdictional framework. First, the authors present some of those new digital economy assets and canvass several typical cross-border challenges inherent in international investment arbitration. Second, they question how traditional objections to jurisdiction ratione personae and jurisdiction ratione materiae might be employed when the investments in question relate to those digital developments. Third, the chapter raises questions about states’ jurisdiction to prescribe, and ponders the potential effects for purposes of jurisdiction of states asserting their authority to prescribe over investments or investors outside their territory.