Grounded in social-contractual ideas about relationships between the governed and those who govern, the provision of social benefits to citizens has historically been predicated on expectations of acquiescence to state authority. However, the rapid expansion of noncontributory social assistance in sub-Saharan Africa, often supported by global donors through technical assistance programs, raises myriad questions about the relationship between social protection and the social contract in fragile and low-capacity contexts. Focusing on sub-Saharan Africa, but drawing on the theoretical and empirical literature on social protection from around the world, this review parses out the redistributive, contractual, and reconstitutive effects of social protection programming on citizen–state relations. We argue that program features—including targeting, conditionality, accountability mechanisms, bureaucratic reach, and the nature and visibility of state–nonstate partnerships—interact dialectically with existing state–society relationships to engender different social contract outcomes for differently situated populations.

No abstract

This essay draws on qualitative social science to propose a critical intellectual infrastructure for data science of social phenomena. Qualitative sensibilitiesinterpretivism, abductive reasoning, and reflexivity in particular-could address methodological problems that have emerged in data science and help extend the frontiers of social knowledge. First, an interpretivist lens-which is concerned with the

Despite trends towards greater LGBTQ (lesbian, gay, bisexual, transgender, and queer) rights in industrialized democracies, the rights of sexual minorities have become increasingly politicized and restricted throughout Africa. Recognizing religion's central role in shaping attitudes toward gays and lesbians, we hypothesize that local religious diversity could expose individuals to alternative religious perspectives, engender tolerance toward marginalized communities, and therefore dislodge dogmatic beliefs about social issues. Employing cross-national Afrobarometer survey data from 33 countries with an index of district-level religious concentration, we find that respondents living in religiously pluralistic communities are 4–5 points more likely to express tolerance of homosexual neighbors (50% increase) compared to those in homogeneous locales. This effect is not driven by outlier countries, the existence of specific religious affiliations within diverse communities, respondents' religiosity, or other observable and latent factors at the country, sub-national, district, and individual level. Further robustness checks address potential threats to validity. We conclude that religious diversity can foster inclusion of sexual minorities in Africa.

No abstract

Sanctuary city policies seek to protect undocumented community members from federal detention or deportation. Debates over sanctu ary cities have become increasingly prominent and partisan in American politics. Republicans accuse sanctuary cities of enabling crime, while Democrats laud them for protecting communities from rights viola tions. Despite partisan salience, we have little information about peo ples' substantive knowledge of sanctuary policies or how crucial that knowledge is in shaping partisan attitudes toward those policies. Drawing on a unique survey dataset of sanctuary attitudes, we demon strate that an absence of political knowledge has asymmetrical effects on sanctuary attitudes along ideological and partisan lines. Knowledge about sanctuary policies increases support for sanctuary cities among liberals/Democrats, whereas conservatives/Republicans do not require substantive knowledge to align their attitudes on sanctuary cities with their ideological predispositions. This finding advances scholarship on the interplay between political knowledge and ideology, and has impor tant immigrationrelated policy and advocacy implications.Palabras Clave: ciudad santuario, política pública, opinión pública, políticas urbanas, conocimiento político.

Language models increasingly rely on massive web dumps for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and newswire often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new dataset of U.S. high school newspaper articles-written by students from across the country-we investigate whose language is preferred by the quality filter used for GPT-3. We find that newspapers from larger schools, located in wealthier, educated, and urban ZIP codes are more likely to be classified as high quality. We then demonstrate that the filter's measurement of quality is unaligned with other sensible metrics, such as factuality or literary acclaim. We argue that privileging any corpus as high quality entails a language ideology, and more care is needed to construct training corpora for language models, with better transparency and justification for the inclusion or exclusion of various texts. 1 We note that the term quality is often ill-defined in the NLP literature. For example, Brown et al. (2020) and refer to "high-quality text" or "high-quality sources"-both citing Wikipedia as an example-but without explaining precisely what is meant.

No abstract