Who governs—and who should govern—online communication? Social media companies, international organizations, users, or the state? And by what means? A range of rhetorical devices have been used to simplify the complexities associated with the governance of online platforms. This includes “constitutional metaphors”: metaphorical allusions to traditional political concepts such as statehood, democracy, and constitutionalism. Here, we empirically trace the ascent of a powerful constitutional metaphor currently employed in the news media discourse on platform governance: characterizations of Facebook’s Oversight Board (OB) as a “supreme court.” We investigate the metaphor’s descriptive suitability and question its normative and political ramifications. We argue that uncritical characterizations of the OB as Facebook’s “supreme court” obscure its true scope and purpose. In addition, we argue that appropriating the socio-cultural symbolism and hence political legitimacy of a supreme court and mapping it onto a different type of actor poses a threat to responsible platform governance.

Beyond the physical structures that contain daily routines, urban city dwellers repeatedly encounter strangers that similarly shape their environments. Familiar strangers are neither formal acquaintances nor completely anonymous faces in daily urban life. Due to data limitations, there is a lack of research focused on uncovering the structure of the “Familiar Stranger” phenomenon at a large scale while simultaneously investigating the social relationships between such strangers. Using countrywide mobile phone records from Andorra, we empirically show the existence of such a phenomenon as well as details concerning these strangers’ relative social relations. To understand the social and spatial components of familiar strangers more deeply, we study the temporal regularity and spatial structure of collective urban mobility to shed light on the mechanisms that guide these interactions. Furthermore, we explore the relationship between social distances and the number of encounters to show that more significant physical encounters correspond to a shorter social distance. Understanding these social and physical networks has essential implications for epidemics spreading, urban planning, and information diffusion.

ObjectiveTo measure poverty-based disparities in inpatient length of stay for paediatric hospitalisations. In particular, this paper examines the relationship between municipality level poverty rates and length of stay, accounting for individual level characteristics.DesignWe use patient discharge data to conduct a repeated cross-sectional study of the totality of paediatric hospitalisations in 15 regions of Chile, in the years 2011, 2013, 2015 and 2017.SettingAll hospital discharges in 15 regions of Chile.Participants1 033 222 discharges for children under the age of 15, between 2011 and 2017.Outcome measuresLength of stay (LOS); LOS by type of insurance and type of hospital; hospitalisation rates; municipality-level average LOS.ResultsWe find that municipality level poverty rates are a significant predictor of LOS, even after controlling for individual and area level characteristics, including type of insurance. Children from municipalities in the poorest quintile have a LOS that is 14% shorter as compared with children from municipalities in the richest quintile. This relationship is stronger for publicly insured children: the decrease in LOS associated with the same poverty change is of 22%.ConclusionsThis paper shows that there is an association between municipality-level poverty rates and length of stay for paediatric hospitalisations in Chile. For the vast majority of the sample, and after controlling for individual level characteristics, an increase in the municipality level poverty rate is associated with a decrease in the length of stay. Further, there is a non-linearity in the relationship, where at the highest poverty rates, poverty and LOS are positively associated. These findings are robust after controlling for type of hospital (public vs private), type of insurance (public vs private), type of diagnosis, as well as year and region fixed effects.

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