1 - 10 of 19 results for: SOC ; Currently searching summer courses. You can expand your search to include all quarters

The state of California, the so-called 'land of plenty,' in fact has the country's highest poverty rate as well as extremely high rates of homelessness and profound racial and ethnic disparities. These problems persist despite a long history of anti-inequality policy. What should be done? In an innovative $35M experiment, the state of California is testing bold new approaches to taking on poverty and inequality, including an unconditional 'guaranteed income' that assures that everyone can raise their children in healthy environments, invest in their skills, and take advantage of opportunities. The Stanford Guaranteed Income Team - a coalition of Stanford faculty - will be advising on the implementation and evaluation of this experiment (pending the state's final review of their grant application). Would you like to assist with one of the boldest anti-inequality experiments of our time? If you sign on for this course, you will (a) learn about the causes of poverty and other inequities and how they can be taken on, and (b) then assist with the implementation and evaluation of the experiment by interviewing potential participants. This course is not for the faint of heart. It will involve intensive training in qualitative interviewing and other types of research; it will require a commitment to be there for the people who have decided to participate in the experiment; and it will require a willingness to listen and learn with humility and respect.

Can machine learning help businesses hire (or fire) the right people? Can data science be used to close the gender pay gap? In this class, we'll explore the promises and pitfalls of using contemporary data analytics to help organizations manage their human resources. In doing so, we'll carefully examine the cutting-edge tools used by people analysts, use formal perspectives of human organization to think through the possible consequences of implementing these solutions in a business, and reason critically about the societal and ethical implications of their proliferation. No background in data science, computer science, or advanced mathematics is assumed for this class.

Mental health is an increasingly hot topic in the media. Why are high school and college students struggling with more and more mental health issues? Why are rates of depression and anxiety increasing? What is the role of social media? How can people cope with the psychological consequences of a multi-year pandemic? These conversations often culminate in the belief that there is a 'mental health crisis' plaguing the 21st century. But mental health, like other social phenomena, is not experienced in a vacuum. How does social context shape individuals' psychological experience? How might sociologists think about the idea of a mental health crisis? This course will provide an introduction to the sociology of mental health and will give you the tools to think critically about narratives around wellbeing that you may hear in your own lives. You will learn how the line between health and illness ('normal' and 'crazy') is socially constructed, how social context influences subjective experience, and how people's responses to subjective experience can change (and have changed) over time. We will also delve into demographic patterns in mental health experiences and discuss the social stigma that surrounds mental illness, mental health treatment, and diagnosis. Throughout the course, we will discuss contemporary issues around mental health - such as social media and the COVID-19 pandemic - using our sociological lens to offer explanations and insights. You will learn through reading scientific articles and books, class discussions, group work, and an independent final project that will be presented to the class at the end of the term.

What does is mean for something to be 'natural', and why is a connection to nature so often seen as a good thing? Drawing on perspectives from sociology, psychology, anthropology, environmental studies, and popular culture, this course analyzes how the concepts of nature and naturalness contribute to the way we make sense of our social world, including based on race, gender, beauty, morality, and politics. Students will learn about the history of environmentalism, the sociology of bodies, the economics of consumption, and the social psychology of traditionalism. Through a combination of lectures, in-class discussions, written assignments, and group projects, students will be encouraged to interrogate their own relationships with nature in order to more intentionally act towards the natural world and towards other people. Throughout, the course will prioritize a sociological lens by considering the roles of social relationships, power, scientific evidence, and inequality in cultural conceptions of nature.

Data science and machine learning have rapidly gained recognition within the social sciences because they offer powerful new ways to ask questions about social and cultural issues. This course will examine how data science has revolutionized how social scientists study culture by providing new tools to analyze patterns in text data in different contexts and at different scales. More specifically, we will explore how these tools can be used to mine the meaning of text from sources such as posts on social media, transcripts of political debates, books, press releases, and more. This is a hands-on, interactive course culminating in a social data science project designed by the student or a team of up to four students. Most class sessions will be taught interactively using Jupyter Notebooks. Students will follow along with workshop-style lectures by using and modifying the provided Python code in real time to analyze data and visualize results. The course will cover such topics as gender and racial/ethnic stereotypes, workplace discrimination, climate change, and the relationship between lifestyle and political identity. Students will learn to explore text data with tools such as word embeddings, topic models, and sentiment analysis. Students will gain experience with base Python as well as tools from libraries useful for data science such as Empath, Gensim, NumPy, Pandas, Scikit-learn, and spaCy.

There are few issues more important for human life than those concerned with sustainability. Current global trends, such as climate change, biodiversity loss, rising inequality, and increasing urbanization, raise critical questions about future environmental and social sustainability. Changes are necessary for the survival of our species. But how can we bring them about? In this course we explore the historical and cultural diversity of human-environment interaction, and analyze sustainability in a variety of contexts: from the local to the global, in the past and present, in the U.S. and among small-scale societies. We¿ll look at development through the lens of food and agriculture, and discuss sustainability in the context of globalization¿ whether social movements around food justice, or the new world of lab-based meats. From behavioral psychology, and how it contributes to environmental action, to the individual choices we make every day, this course will help you reflect on the world, and how to act in it.

Prior arrangement required.