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  1. Smoking Diffusion through Networks of Diverse, Urban American Adolescents over the High School Period

    This study uses recent data to investigate if smoking initiation diffuses through friendship networks over the high school period and explores if diffusion processes differ across schools. One thousand four hundred and twenty-five racially and ethnically diverse youth from four high schools in Los Angeles were surveyed four times over the high school period from 2010 to 2013. Probit regression models and stochastic actor-based models for network dynamics tested for peer effects on smoking initiation.
  2. Challenging Evolution in Public Schools: Race, Religion, and Attitudes toward Teaching Creationism

    Researchers argue that white evangelical Christians are likely to support teaching creationism in public schools. Yet, less is known about the role religion may play in shaping attitudes toward evolution and teaching creationism among blacks and Latinos, who are overrepresented in U.S. conservative Protestant traditions. This study fills a gap in the literature by examining whether religious factors (e.g., religious affiliation and Biblical literalism) relate to differences in support for teaching creationism between blacks and Latinos compared to whites and other racial groups.
  3. Preventing Violence: Insights from Micro-Sociology

    Micro-sociology of violence looks at what happens in situations where people directly threaten violence, but only sometimes carry it out. This process and its turning points have become easier to see in the current era of visual data: cell-phone videos, long-distance telephoto lenses, CCTV cameras. New cues and instruments are on the horizon as we look at emotional signals, body rhythms, and monitors for body signs such as heart rate (a proxy for adrenaline level).
  4. Predicting GPA at Age 15 in the Fragile Families and Child Wellbeing Study

    In this paper, we describe in detail the different approaches we used to predict the GPA of children at the age of 15 in the context of the Fragile Families Challenge. Our best prediction improved about 18 percent in terms of mean squared error over a naive baseline prediction and performed less than 5 percent worse than the best prediction in the Fragile Families Challenge. After discussing the different predictions we made, we also discuss the predictors that tend to be robustly associated with GPA. One remarkable predictor is related to teacher observations at the age of nine.
  5. Friend Request Pending: A Comparative Assessment of Engineering- and Social Science–Inspired Approaches to Analyzing Complex Birth Cohort Survey Data

    The Fragile Families Challenge is a mass collaboration social science data challenge whose aim is to learn how various early childhood variables predict the long-term outcomes of children. The author describes a two-step approach to the Fragile Families Challenge. In step 1, a variety of fully automated approaches are used to predict child academic achievement. In total 124 models are fit, which involve most possible combinations of eight model types, two imputation strategies, two standardization approaches, and two automatic variable selection techniques using two different thresholds.
  6. Winning Models for Grade Point Average, Grit, and Layoff in the Fragile Families Challenge

    In this article, the authors discuss and analyze their approach to the Fragile Families Challenge. The data consisted of more than 12,000 features (covariates) about the children and their parents, schools, and overall environments from birth to age 9.
  7. Diverging Trajectories or Parallel Pathways? An Intersectional and Life Course Approach to the Gender Earnings Gap by Race and Education

    Integrating ideas about intersectionality with life course theories, we explore how trajectories of gender earnings inequality vary across race and education. Past research suggests that gender earnings gaps by race and education are narrower for more disadvantaged groups, yet it remains unknown whether these key differences amplify, decline, or remain constant over the life course.
  8. CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media

    Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)—a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China).
  9. The Organizational Ecology of College Affordability: Research Activity, State Grant Aid Policies, and Student Debt at U.S. Public Universities

    Sociologists have theorized U.S. universities as a heterogenous organizational ecology. We use this lens to compare student debt and college prices for low-income students across public universities according to their research intensiveness and varied state grant aid policies. We show that students at research-intensive public universities have had an easier time repaying student loans than at other schools.
  10. Industry, Firm, Job Title: The Layered Nature of Early-Career Advantage for Graduates of Elite Private Universities

    Using concepts associated with effectively maintained inequality theory and horizontal stratification, the authors ask whether the private-public dividing line is a “threshold of consequence” for early-career market entry. To address this empirically, the authors use a novel LinkedIn data set to analyze job pathways for the graduating class of 2016 from the top 25 private and top 25 public universities in the United States.