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In the early twentieth century, Asian Americans and Latinos organized along national origin lines and focused on assimilation; By the 1960s and 1970s, community organizers from both groups began to form panethnic community service organizations (CSOs) that emphasized solidarity. I argue that focusing on the rise of panethnic CSOs reveals an underappreciated mechanism that has mobilized Asian Americans and Latinos—the welfare state. The War on Poverty programs incentivized non-black minority community organizers to form panethnic CSOs to gain access to state resources and serve the economically disadvantaged in their communities. Drawing on extensive archival research, I identify this mechanism and test it with my original dataset of 818 Asian American and Latino advocacy organizations and CSOs. Leveraging the Reagan budget cut, I show that dismantling the War on Poverty programs reduced the founding rate of panethnic CSOs. I further estimated that a 1 percent increase in federal funding was associated with the increase of the two panethnic CSOs during the War on Poverty. The findings demonstrate how access to state resources forces activists among non-primary beneficiary groups to build new political identities that fit the dominant image of the policy beneficiaries.

Scholars have long argued that the marginalized racial status shared by ethnic minority groups is a strong incentive for mobilization and coalition building in the United States. However, despite their members' shared racial status as "Orientals," different types of housing coalitions were formed in the Chinatowns of San Francisco, Seattle, and Vancouver during the 1960s and 1970s. Asian race-based coalitions appeared in San Francisco and Seattle, but not in Vancouver, where a cross-racial coalition was built between the Chinese and southern and eastern Europeans. Drawing on exogenous shocks and process tracing, this article explains how historical legacies—specifically, the political geography of settlement—shaped this divergence. These findings demonstrate how long-term historical analysis offers new insights into the study of minority coalition formation in the United States.

Although North Korean refugees are co-ethnics and receive extensive government support, many reports have alleged that they are discriminated against in South Korea. We theorize that this outcome is due to the contested status of North Korean refugees' co-ethnic identity. We test this with embedded list experiments on an approximately nationally representative sample of South Koreans (n = 1418). We find that (1) South Koreans hold prejudice against North Korean refugees primarily because they believe supporting these refugees would waste government resources; (2) liberals are more likely to disagree with this belief while conservatives are ambivalent, and (3) income predicts this belief better than partisanship or ideology. Scholars have focused on native-refugee conflict cases where ethnic identity is a faultline. However, the South Korean case shows that co-ethnic refugees could be unwelcomed in a host society if political and economic factors make their co-ethnic status contested.

We examine the ways in which change in civil society has contributed to the erosion of democracy in the United States. Democracy demands that people commit to pluralistic self-determination, which means that people must be willing to seek power and also share it. We argue that civil society plays two important roles in sustaining people's willingness to do both: first, civil society cultivates a capacity for expressing choice; and second, it teaches capacities and provides opportunities for people to negotiate power. We show that in recent decades, civil society's emphasis has moved more toward expressing choice and away from the creation of venues for negotiating power. We conclude with recommendations for researchers, civil society leaders, funders, and policy-makers who are interested in committing to forms of civil society that take power seriously.

Linked fate, the concept introduced by Dawson almost three decades ago, reoriented the study of racism and political behavior in the United States. The scholarship traditionally had focused on the racial psychology of whites and how racism seeps into their political views and actions. Dawson proposed the Black utility heuristic theory and linked fate, its associated measure, to investigate the political behavior of Blacks, the minority group most harmed by racism. Since then, linked fate has become a ubiquitous variable of interest in research on minority group politics. Yet the research program around linked fate is due for some extension. Most studies gloss over the fact that the Black utility heuristic theory is historically and socially conditional. We call for bringing elite-level agency and group-level social practices back into the literature to clarify the macro- and meso-level conditions under which a group's racial status translates into linked fate at the individual level. Greater inquiry into these dynamics is not only warranted but also has broad implications for the research on racial and ethnic politics.

ABSTRACTComputational 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.

AbstractThe aim of this paper is to compare the focus (strategy, capacity, and operation) and locus (internal and external) of innovation types of Sustainable Development Goals (SDGs)‐oriented public sector innovation (PSI) in Thailand and Korea and to contribute to the limited understanding of the role of national context in PSI. Our study analysed 263 SDGs‐oriented innovations based on the new typology proposed by Chen et al. The findings identified that the orientation of SDGs‐oriented PSI is more external and policy innovation is the most common type in both countries. These distributions, however, vary depending on the contextual differences in administrative and technological contexts, resulting in SDGs‐oriented PSI in Korea emphasised on strategy focus, whereas Thailand emphasised capacity focus. This also demonstrates a temporality between strategy, capacity, and operations foci in Korea, but Thailand attempted to fill the capacity gap through SDGs‐oriented innovation. Insights from this empirical study can assist public managers in selecting innovation portfolio configurations applicable to their national context.Points for practitioners
In executing public sector innovation, public sector organisations (PSOs) should consider the innovation focus (strategy, capacity, and operation) and the innovation locus (internal and external).
For SDGs‐oriented innovation, mission and policy innovation should introduce the necessary strategies in the public services before capacity and operation focus.
Public managers and practitioners should adopt an innovation portfolio approach to develop and introduce a variety of innovation types.
Public managers should consider their national context to select the configuration of their innovation portfolios.

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.

The advent of social media has increased digital content - and, with it, hate speech. Advancements in machine learning help detect online hate speech at scale, but scale is only one part of the problem related to moderating it. Machines do not decide what comprises hate speech, which is part of a societal norm. Power relations establish such norms and, thus, determine who can say what comprises hate speech. Without considering this data-generation process, a fair automated hate speech detection system cannot be built. This chapter first examines the relationship between power, hate speech, and machine learning. Then, it examines how the intersectional lens - focusing on power dynamics between and within social groups - helps identify bias in the data sets used to build automated hate speech detection systems.

ABSTRACTSocial scientists with data science skills increasingly are assuming positions as computational social scientists in academic and non-academic organizations. However, because computational social science (CSS) is still relatively new to the social sciences, it can feel like a hidden curriculum for many PhD students. To support social science PhD students, this article is an accessible guide to CSS training based on previous literature and our collective working experiences in academic, public-, and private-sector organizations. We contend that students should supplement their traditional social science training in research design and domain expertise with CSS training by focusing on three core areas: (1) learning data science skills, (2) building a portfolio that uses data science to answer social science questions, and (3) connecting with computational social scientists. We conclude with practical recommendations for departments and professional associations to better support PhD students.