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| Srl | Item |
| 1 |
ID:
186657
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| Summary/Abstract |
Extending theories of social exclusion and elite messaging, we argue that Trump’s targeted rhetoric toward Asian Americans during the COVID-19 pandemic pushes the racial group, largely “Independent” or nonpartisan affiliated, to lean more towards the Democratic Party. We support this claim by combining social media (Study 1) and survey data (Study 2) analysis. Tracing 1.4 million tweets, we find that Trump’s rhetoric has popularized racially charged coronavirus-related terms and that exclusionary, anti-Asian attitudes have increased in the United States since the pandemic began. Next, by analyzing repeated cross-sectional weekly surveys of Asian Americans from July 2019 to May 2020 (n=12,907), we find that the group has leaned more towards the Democratic Party since Trump first made inflammatory remarks towards Asian Americans. Whites, Blacks, and Latina/os, on the other hand, exhibited fewer and less consistent changes in Democratic Party-related attitudes. Our findings suggest that experiences with social exclusion that are driven by elite sources further cement Asian Americans as Democrats.
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| 2 |
ID:
186718
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| Summary/Abstract |
Computational methods have become an integral part of political science research. However, helping students to acquire these new skills is challenging because programming proficiency is necessary, and most political science students have little coding experience. This article presents pedagogical strategies to make transitioning from Excel, SPSS, or Stata to R or Python for data analytics less challenging and more exciting. First, it discusses two approaches for making computational methods accessible: showing the big picture and walking through the workflow. Second, a step-by-step guide for a typical course is provided using three examples: learning programming fundamentals, wrangling messy data, and communicating data analysis.
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