This report aims to 'crack the code' by deciphering the factors that hinder and facilitate girls' and women's participation, achievement and continuation in science, technology, engineering and mathematics (STEM) education and, in particular, what the education sector can do to promote girls' and women's interest in and engagement with STEM education and ultimately STEM careers.
The transformation of women's lives over the past century is among the most significant and far-reaching of social and economic phenomena, affecting not only women but also their partners, children, and indeed nearly every person on the planet. In developed and developing countries alike, women are acquiring more education, marrying later, having fewer children, and spending a far greater amount of their adult lives in the labor force. Yet, because women remain the primary caregivers of children, issues such as work-life balance and the glass ceiling have given rise to critical policy discussions in the developed world. In developing countries, many women lack access to reproductive technology and are often relegated to jobs in the informal sector, where pay is variable and job security is weak. Considerable occupational segregation and stubborn gender pay gaps persist around the world. The Oxford Handbook of Women and the Economy is the first comprehensive collection of scholarly essays to address these issues using the powerful framework of economics. Each chapter, written by an acknowledged expert or team of experts, reviews the key trends, surveys the relevant economic theory, and summarizes and critiques the empirical research literature. By providing a clear-eyed view of what we know, what we do not know, and what the critical unanswered questions are, this Handbook provides an invaluable and wide-ranging examination of the many changes that have occurred in women's economic lives.
"In an era when women are increasingly prominent in medicine, law and business, why are there so few women scientists and engineers? A new research report by AAUW presents compelling evidence that can help to explain this puzzle. Why So Few? Women in Science, Technology, Engineering, and Mathematics presents in-depth yet accessible profiles of eight key research findings that point to environmental and social barriers - including stereotypes, gender bias and the climate of science and engineering departments in colleges and universities - that continue to block women's participation and progress in science, technology, engineering, and math. The report also includes up to date statistics on girls' and women's achievement and participation in these areas and offers new ideas for what each of us can do to more fully open scientific and engineering fields to girls and women."--pub. desc.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Females consistently score lower than males on standardized tests of mathematics - yet no such differences exist in the classroom. These differences are not trivial, nor are they insignificant. Test scores help determine entrance to college and graduate school and therefore, by extension, a person's job and future success. If females receive lower test scores then they also receive fewer opportunities. Why does this discrepancy exist? This book presents a series of papers that address these issues by integrating the latest research findings and theories. Authors such as Diane Halpern, Jacquelynne Eccles, Beth Casey, Ronald Nuttal, James Byrnes, and Frank Pajares tackle these questions from a variety of perspectives. Many different branches of psychology are represented, including cognitive, social, personality/self-oriented, and psychobiological. The editors then present an integrative chapter that discusses the ideas presented and other areas that the field should explore.
This book reports on a three-year project (2017-2019) funded by the International Science Council and involving eleven scientific partner organizations. The main goal of the project was to investigate the gender gap in STEM disciplines from different angles, globally and across disciplines. We have performed (i) a global survey of scientists with more than 32,000 responses; (ii) an investigation of the effect of gender in millions of scientific publications; and (iii) the compilation of best-practice initiatives that address the gender gap in Mathematical, Computing, and Natural Sciences at various levels. We conclude that the gender gap is very real in science and mathematics. We present methodologies, insights, and tools that have been developed throughout the project, as well as a set of recommendations for different audiences: instructors and parents; educational institutions; scientific unions and other organizations responsible for science policy.
This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
Dreamscapes of Modernity offers the first book-length treatment of sociotechnical imaginaries, a concept originated by Sheila Jasanoff and developed in close collaboration with Sang-Hyun Kim to describe how visions of scientific and technological progress carry with them implicit ideas about public purposes, collective futures, and the common good. The book presents a mix of case studies—including nuclear power in Austria, Chinese rice biotechnology, Korean stem cell research, the Indonesian Internet, US bioethics, global health, and more—to illustrate how the concept of sociotechnical imaginaries can lead to more sophisticated understandings of the national and transnational politics of science and technology. A theoretical introduction sets the stage for the contributors’ wide-ranging analyses, and a conclusion gathers and synthesizes their collective findings. The book marks a major theoretical advance for a concept that has been rapidly taken up across the social sciences and promises to become central to scholarship in science and technology studies.
