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Forthcoming in American Behavioral Scientist. The structured inequalities built into our sociotechnical institutions shape access to knowledge. During the COVID-19 pandemic, knowledge acquisition was shaped by news sources, class, and race. Through analysis of nationally representative data using logistic models, this study reveals how the use of different news sources differentially shapes access to accurate knowledge about COVID-19 topics for different demographics. Those who rely on informal and local news sources have the largest knowledge gaps about these topics, while those who seek information from national or international news outlets and politicians have the most accurate knowledge. Race and class influence knowledge of government operations, public health, and science of COVID-19. In particular, Black people, people with less education, and those with lower incomes are significantly less likely to have accurate knowledge about COVID-19, all else equal. These findings have implications for knowledge dissemination that impacts public health, as well as for how news media target different audiences in an increasingly fractured landscape.

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, skewing coefficients, biasing standard errors, and yielding inconsistent model estimates. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.

This report is a high-level summary analysis of the 2017 GitHub Open Source Survey dataset, presenting frequency counts, proportions, and frequency or proportion bar plots for every question asked in the survey., Comment: 58 pages