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Social movement mobilization by and on behalf of immigrants occurs frequently today, but sociologists have been slow to include immigrant collective action in the canon of social movement or immigration scholarship. Is this because, until recently, immigrant protest was minimal or limited in scope? The authors take a macro-comparative approach, recoding the Dynamics of Collective Action dataset to compare proimmigrant collective action with paradigmatic, well-studied movements from 1960 to 1995. The authors find that immigrant collective action was on par with iconic movements, mobilized similar numbers of people, occurred across the United States, engaged in disruptive action, and encompassed a wide range of origins, thus correcting possible misperceptions that immigrants did not engage in contentious action before the 1990s. The authors conclude by advocating for a population at risk focus for studying the emergence of collective action, decentering the borders of collective mobilization, and illuminating the vulnerabilities of legal status.

Nonprofits provide a range of human and social services in the United States, producing what some call the delegated welfare state. The authors aim to quantify inequities in nonprofit service provision by focusing on two types of vulnerabilities: spatial and socio-demographic. Specifically, the authors develop a service accessibility index to identify mismatch between population demand and locational supply of nonprofits. The authors apply the index to an original data set of more than 1,500 immigrant-serving legal and health organization in California, Nevada, and Arizona. The authors find that immigrants living in rural areas are underserved, especially in access to justice, compared with those in metropolitan areas but that residents of smaller cities have better access, especially to health services, than those in larger cities. The service accessibility index not only brings such inequities into relief but raises critical questions about the determinants and consequences of service-access variability, for vulnerable immigrants and others dependent on the nonprofit safety net.

Many migrants are vulnerable due to noncitizenship, linguistic or cultural barriers, and inadequate safety-net infrastructures. Immigrant-oriented nonprofits can play an important role in improving immigrant well-being. However, progress on systematically evaluating the impact of nonprofits has been hampered by the difficulty in efficiently and accurately identifying immigrant-oriented nonprofits in large administrative data sets. We tackle this challenge by employing natural language processing (NLP) and machine learning (ML) techniques. Seven NLP algorithms are applied and trained in supervised ML models. The bidirectional encoder representations from transformers (BERT) technique offers the best performance, with an impressive accuracy of .89. Indeed, the model outperformed two nonmachine methods used in existing research, namely, identification of organizations via National Taxonomy of Exempt Entities codes or keyword searches of nonprofit names. We thus demonstrate the viability of computer-based identification of hard-to-identify nonprofits using organizational name data, a technique that may be applicable to other research requiring categorization based on short labels. We also highlight limitations and areas for improvement.