What is a counterrevolution? And how often do they occur? Chapter 2 is devoted to answering these foundational questions. According to this book, a counterrevolution is an irregular effort in the aftermath of a successful revolution to restore a version of the pre-revolutionary political regime. The chapter begins by explaining and contextualizing this definition. It reviews the various alternative understandings of counterrevolution that have been invoked by both scholars and activists. It then explains the decision to adopt a definition of counterrevolution as restoration and shows how this definition was operationalized in building the original dataset. The second half of the chapter lays out the main high-level findings from this dataset. About half of all revolutionary governments have faced a counterrevolutionary challenge of some type, and roughly one in five of these governments was successfully overturned. Moreover, these counterrevolutions have been distributed unevenly: the vast majority have toppled democratic revolutions, rather than ethnic or leftist ones. And counterrevolutions had for years been declining in frequency, until the last decade when this trend reversed. These descriptive findings provide the motivation for the theory developed in Chapter 3.

The advent of the digital age has brought about significant changes in how information is created, disseminated and consumed. Recent developments in the use of big data and artificial intelligence (AI) have brought all things digital into sharp focus. Big data and AI have played pivotal roles in shaping the digital landscape. The term ‘big data’ describes the vast amounts of structured and unstructured data generated every day. Advanced analytics on big data enable businesses and organisations to extract valuable insights, make informed decisions and enhance various processes. AI, on the other hand, has brought about a paradigm shift in how machines learn, reason and perform tasks traditionally associated with human intelligence. Machine-learning algorithms, a subset of AI, process vast datasets to identify patterns and make predictions. This has applications across diverse fields, including health care, finance, marketing and more. The combination of big data and AI has fuelled advancements in areas such as personalised recommendations, predictive analytics and automation in all aspects of our day-to-day lives.

This chapter presents material on statistics, from the basic principles of the classification of data to normal distribution. We go on to discuss types of the null hypothesis, types of error and different methods of statistical analysis depending on the type of data set presented.

As payment is increasingly becoming part of social media, it takes on the operating, governance, and revenue models of the Silicon Valley tech industry. At the same time, new platform payments “ride the rails” of long-standing infrastructures. These conditions create opportunities for surveillance and infrastructural power, as well as new unanticipated harms for users. As the future of money is imagined, it is wise to contemplate a payment ecosystem that is – like social media more broadly – increasingly private, siloed, and rife with scams.

The production of knowledge in public health involves a systematic approach that combines imagination, science, and social justice, based on context, rigorous data collection, analysis, and interpretation to improve health outcomes and save lives. Based on a comprehensive understanding of health trends and risk factors in populations, research priorities are established. Rigorous study design and analysis are critical to establish causal relationships, ensuring that robust evidence-based interventions guide beneficial health policies and practice. Communication through peer-reviewed publications, community outreach, and stakeholder engagement ensures that insights are co-owned by potential beneficiaries. Continuous monitoring and feedback loops are vital to adapt strategies based on emerging outcomes. This dynamic process advances public health knowledge and enables effective interventions. The process of addressing a complex challenge of preventing HIV infection in young women in sub-Saharan Africa, a demographic with the least social power but the highest HIV risk, highlights the importance of inclusion in knowledge generation, enabling social change through impactful science.

A fundamental problem in descriptive epidemiology is how to make meaningful and robust comparisons between different populations, or within the same population over different periods. The problem has several dimensions. First, the data we have to work with (e.g. incident and prevalent cases, and deaths) is rarely usable in its raw form. We must therefore transform it in some way before undertaking the comparison itself. Second, our data usually tells us about fundamentally different attributes of the populations we are seeking to compare. If we are only ever interested in comparing any one of these attributes at a time (mortality, for example), then one of several simple and well-established transformations is all that is typically required. Increasingly, however, epidemiologists are being asked to bring these attributes together into more integrated and meaningful comparisons.

In this chapter, we review approaches to model climate-related migration including the multiple goals of modeling efforts and why modeling climate-related migration is of interest to researchers, commonly used sources of climate and migration data and data-related challenges, and various modeling methods used. The chapter is not meant to be an exhaustive inventory of approaches to modeling climate-related migration, but rather is intended to present the reader with an overview of the most common approaches and possible pitfalls associated with those approaches. We end the chapter with a discussion of some of the future directions and opportunities for data and modeling of climate-related migration.

The fast-growing clinical research outsourcing over the course of the past decade has attracted the active involvement of private equity buyers to the “business” of Clinical Research Organizations (CROs). The fragmentation of pharmaceutical services through CROs offers an opportunity to lower the financial risk of drug development in a highly critical industry. Justified by the operational efficiency that integrating assets and services to decrease costs even further may represent, private equity has recently demonstrated an appetite for vertical integrations to expand the geographic, patient, and service reach of clinical research sites. Since private equity managers have the incentive to maximize financial returns, gaining greater access and control of patient identification, enrollment and retention, and quality protocols may pose significant equity-based risks to biopharmaceutical stakeholders, fundamentally, drug end users.

The proliferation of CROs and their plans to gain further operational control of clinical trials pose the potential tradeoff between drug development efficiency and equitable development and access to drugs and research knowledge. This reasonable concern is most present in the efficiency-based scholarship debate on shareholder and stakeholder governance of recent years. The discussion centers on whether new corporate leaders and investors might be paying too much attention to shareholder value while not enough to stakeholder value. A balance, the debate seems to suggest, is critical to generating long-term shareholder wealth, shifting the long-standing “shareholder value” corporate governance model toward an equity-based, “deliver value” type, commitment.

By observing the lay of the land of CROs in the United States and using as a theoretical framework the increasingly influential stakeholderism approach to corporate governance, this essay offers a critical view of the use of stakeholder factors in stewardship decisions made by CROs’ corporate leaders and private equity investors. Ultimately, it aims to identify key areas of organizational and governance concerns in medical research and the shortcomings they represent to equitable access to medical innovation.

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.