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Chapter 1 examines the evolving phenomenon of corporate governing, distinguishing between two forms: government substitution, where corporations fill gaps left by public institutions, and corporate socioeconomic advocacy, where firms engage in public discourse on contested issues. Section A traces the historical arc of corporations taking on public functions, particularly under external pressure during the Progressive and Civil Rights eras. Section B surveys the landscape of contemporary initiatives – spanning racial equity, women’s and reproductive rights, LGBTQ+ advocacy, climate, voting rights, and gun control – and highlights both prosocial and conservative forms of corporate governing. It illustrates how firms like JPMorgan Chase, Apple, and Microsoft have acted on these fronts, and how some like Meta and Amazon have scaled back in the face of political resistance. Section C considers the growing conservative backlash – accelerated by recent legal developments and federal political shifts – and evaluates whether corporate governing is in retreat or undergoing recalibration. The chapter captures the scope, complexity, and volatility of this corporate transformation.
Chapter 3 provides a legal analysis of corporate governing, concluding that it poses minimal challenges under traditional corporate law. It introduces a framework for assessing corporate actions and focuses on two forms: government substitution and corporate socioeconomic advocacy. Courts have treated government substitution as a matter of business discretion, protected by the business judgment rule. Socioeconomic advocacy, while more politically charged, is likewise shielded – reinforced by Simeone v. The Walt Disney Company, where the Delaware Chancery Court denied a shareholder inspection request tied to Disney’s opposition to Florida’s “Don’t Say Gay” law. The decision reflects Delaware courts’ reluctance to intervene in corporate political speech absent credible evidence of fiduciary breaches. Although director liability under Caremark has expanded in other contexts, courts have shown little interest in extending it to political risk absent clear legal violation or egregious facts. That said, legal exposure remains in adjacent domains – particularly securities fraud and ESG/DEI compliance – where heightened regulatory and political scrutiny continues to evolve.
Chapter 4 explores the normative foundations of corporate governing by addressing four central questions: whether there is a business case for it, whether it is strategically sound, whether it benefits social activists and society, and whether it poses risks to democracy. The analysis suggests that corporate governing may enhance firm value, depending on factors like authenticity, stakeholder alignment, and market context. Strategically, it is a voluntary choice, often undertaken with risk assessments in place, though not immune to backlash. While some firms benefit, others may falter, but absent clear evidence of systemic harm, business-based objections are difficult to sustain. The chapter then considers whether corporate governing serves broader social goals. Activists are capable of evaluating the risks of collaboration, and the societal impact should be judged pragmatically, based on outcomes. Finally, the chapter addresses the political dimension – specifically, the democratic risks of delegating public functions to private actors with limited accountability.
In Corporate Power and the Politics of Change, Matteo Gatti examines how corporations have taken on roles traditionally reserved for governments – advocating on social issues, setting internal norms, and stepping in where public institutions fall short. This phenomenon, called corporate governing, takes two forms: socioeconomic advocacy, when companies take public stances, and government substitution, when they deliver services or protections the state does not provide. Drawing on legal doctrine and insights from the social sciences, Gatti shows how this shift reflects broader pressures within firms and deep dysfunction outside them. The rise of corporate governing has also triggered political, legal, and cultural backlash that challenges its legitimacy and reach. Clear-eyed and timely, this book offers a framework for understanding how corporate power reshapes policymaking and what that means for business and democracy.
Confidently analyze, interpret and act on financial data with this practical introduction to the fundamentals of financial data science. Master the fundamentals with step-by-step introductions to core topics will equip you with a solid foundation for applying data science techniques to real-world complex financial problems. Extract meaningful insights as you learn how to use data to lead informed, data-driven decisions, with over 50 examples and case studies and hands-on Matlab and Python code. Explore cutting-edge techniques and tools in machine learning for financial data analysis, including deep learning and natural language processing. Accessible to readers without a specialized background in finance or machine learning, and including coverage of data representation and visualization, data models and estimation, principal component analysis, clustering methods, optimization tools, mean/variance portfolio optimization and financial networks, this is the ideal introduction for financial services professionals, and graduate students in finance and data science.
While most programmes in neuroscience are understandably built around imparting foundational knowledge of cell biology, neurons, networks and physiology, there is less attention paid to critical perspectives on methods. This book addresses this gap by covering a broad array of topics including the philosophy of science, challenges of terminology and language, reductionism, and social aspects of science to challenge claims to explanation and understanding in neuroscience. Using examples from dominant areas of neuroscience research alongside novel material from systems that are less often presented, it promotes the general need of scientists (and non-scientists) to think critically. Chapters also explore translations between neuroscience and technology, artificial intelligence, education, and criminology. Featuring accessible material alongside further resources for deeper study, this work serves as an essential resource for undergraduate and graduate courses in psychology, neuroscience, and biological sciences, while also supporting researchers in exploring philosophical and methodological challenges in contemporary research.
Students have an almost insurmountable task in understanding statistics in the psychological sciences and applying them to a research study. This textbook tackles this source of stress by guiding students through the research process, start to finish, from writing a proposal and performing the study, to analysing the results and creating a report and presentation. This truly practical textbook explains psychology research methods in a conversational style, with additional material of interest placed in focus boxes alongside, so that students don't lose their way through the steps. Every step is detailed visually with processes paralleled in both SPSS and R, allowing instructors and students to learn both statistical packages or to bridge from one to the other. Students perform hands-on statistical exercises using real data, and both qualitative and mixed-methods research are covered. They learn effective ways to present information visually, and about free tools to collect and analyse data.
This book provides a clear and accessible introduction to ring theory for undergraduate students. Aligned with standard curricula, it simplifies abstract concepts through structured explanations, practical examples, and real-world applications. Ideal for both students and instructors, it serves as a valuable resource for mastering fundamental concepts in ring theory with ease. The text begins with an introduction to rings and goes on to cover subrings, integral domains, ideals, and factor rings. It also discusses ring homomorphisms and polynomial rings. The book concludes with topics such as polynomial factorization and divisibility in integral domains. Each chapter is supplemented with solved examples to foster a deeper understanding of the subject. A set of practice questions is also provided to sharpen problem-solving skills.
Designed for graduate students, instructors, and seasoned researchers, this is an essential guide for robust research design and methodology in applied linguistics, covering qualitative, quantitative, and mixed methods research. It adopts a structured approach, starting with the foundational principles of research design, methodology, and data collection and analysis, to writing and interpreting, explaining, and reporting research results, bringing together all the steps and processes of research from start to finish in one single volume in a way that is practical, easy to follow, and easy to understand. Throughout, the emphasis is on the process of research and the application of various research techniques and principles across different areas. These characteristics, coupled with numerous pedagogical features such as key term reviews, visuals, research scenarios, and many discussion and activity questions, make the book an indispensable reference and a valuable textbook for courses in second language and applied linguistics research.
Build a firm foundation for studying statistical modelling, data science, and machine learning with this practical introduction to statistics, written with chemical engineers in mind. It introduces a data–model–decision approach to applying statistical methods to real-world chemical engineering challenges, establishes links between statistics, probability, linear algebra, calculus, and optimization, and covers classical and modern topics such as uncertainty quantification, risk modelling, and decision-making under uncertainty. Over 100 worked examples using Matlab and Python demonstrate how to apply theory to practice, with over 70 end-of-chapter problems to reinforce student learning, and key topics are introduced using a modular structure, which supports learning at a range of paces and levels. Requiring only a basic understanding of calculus and linear algebra, this textbook is the ideal introduction for undergraduate students in chemical engineering, and a valuable preparatory text for advanced courses in data science and machine learning with chemical engineering applications.
Play has a significant role in children's learning and development. Play in the Early Years examines the central questions about play from the perspectives of children, families and educators, providing a comprehensive introduction to the theory and practice of play for children from birth to eight years. In its fourth edition, Play in the Early Years has been thoroughly updated in line with the revised Early Years Learning Framework and the new version of the Australian Curriculum. It takes both a both a theoretical and a practical approach, and covers recent research into conceptual play and wellbeing. The text looks at social, cultural and institutional approaches to play, and explores a range of strategies for successfully integrating play into early years settings and primary classrooms. Each chapter features case studies and play examples, with questions and reflection activities incorporated throughout to enhance learners' understanding.