141 - 150 of 226 results for: SOC

Human decision making is increasingly being displaced by predictive algorithms. Judges sentence defendants based on statistical risk scores; regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. Course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fairer systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises. Admission by consent of instructor and limited to 20 students. To enroll complete course application by March 15 at:  https://5harad.com/mse330/. Grading based on: response papers, class participation, and a final project.

This course provides a comprehensive introduction to the way crime has been defined and punished in the United States. Recent social movements such as the Movement for Black Lives have drawn attention to the problem of mass incarceration and officer-involved shootings of people of color. These movements have underscored the centrality of the criminal justice system in defining citizenship, race, and democracy in America. How did our country get here? This course provides a social scientific perspective on America¿s past and present approach to crime and punishment. Readings and discussions focus on racism in policing, court processing, and incarceration; the social construction of crime and violence; punishment among the privileged; the collateral consequences of punishment in poor communities of color; and normative debates about social justice, racial justice, and reforming the criminal justice system. Students will learn to gather their own knowledge and contribute to normative debates through a field report assignment and an op-ed writing assignment.

Formulating a research question, developing hypotheses, probability and non-probability sampling, developing valid and reliable measures, qualitative and quantitative data, choosing research design and data collection methods, challenges of making causal inference, and criteria for evaluating the quality of social research. Emphasis is on how social research is done, rather than application of different methods. Limited enrollment; preference to Sociology and Urban Studies majors, and Sociology coterms.

Preference to Sociology majors and minors. Enrollment for non-sociologists will open two weeks after winter enrollment begins. Methods for analyzing and evaluating quantitative data in sociological research. Students will be taught how to run and interpret multivariate regressions, how to test hypotheses, and how to read and critique published data analyses.

Digital communications (including social media) are the largest data sets of our time, and most of it is text. Social scientists need to be able to digest small and big data sets alike, process it and extract psychological insight. This applied and project-focused course introduces students to a Python codebase developed to facilitate text analysis in the social sciences (see dlatk.wwbp.org -- knowledge of Python is helpful but not required). The goal is to practice these methods in guided tutorials and project-based work so that the students can apply them to their own research contexts and be prepared to write up the results for publication. The course will provide best practices, as well as access to and familiarity with a Linux-based server environment to process text, including the extraction of words and phrases, topics and psychological dictionaries. We will also practice the use of machine learning based on text data for psychological assessment, and the further statistical analysis of language variables in R. Familiarity with Python is helpful but not required. Basic familiarity with R is expected. The ability to wrangle data into a spreadsheet-like format is expected. A basic introduction to SQL will be given in the course. Familiarity with SSH and basic Linux is helpful but not required. Understanding of regression is expected.

In the contemporary United States, supposedly race-neutral immigration laws have racially-unequal consequences. Immigrants from Mexico, Central America, and the Middle East are central to ongoing debates about who's includable, and who's excludable, from American society. These present-day dynamics mirror the historical forms of exclusion imposed on immigrants from places as diverse as China, Eastern Europe, Ireland, Italy, Japan, and much of Africa. These groups' varied experiences of exclusion underscore the long-time encoding of race into U.S. immigration policy and practice. Readings and discussions center on how immigration law has become racialized in its construction and in its enforcement over the last 150 years.

Prior arrangement required