This article examines how a resource bonanza in Ordos Municipality, Inner Mongolia Autonomous Region, in the 2000s translated into broad-based participation in informal lending networks and drove a remarkable, but short-lived, phase of urban expansion and private wealth accumulation. Local lending networks inflated enormous credit and property bubbles, which ultimately burst in 2011 with severe ramifications for Ordos's urban expansion and households' livelihoods. Based on fifteen months of ethnographic fieldwork in Ordos, the analysis advances the idea of “taking part” to explore the interconnected desires to participate in urban expansion and to seize for oneself a portion of the local bonanzas in natural resources, property, and finance. The socio-spatial impacts of the financialization of the everyday environment are discussed in relation to urban transformation, the production of a “ghost town,” and local authorities' opportunistic forms of deregulation. These were vital ingredients in Ordos's spectacular passage from boom to bust.
This article explores virtual common singing in the time of partial lockdown in Denmark through an auto-ethnographic account. The phenomenon of singing together on Danish public service television gained immense popularity as a response to the pandemic as one-fifth of the population tuned in, in many cases broadcasting themselves while signing. Looking at common singing as an emergent ‘infrastructure for troubling times’, this article takes up questions of digitally mediated intimacy during the COVID-19 lockdown, exploring who sings, what is sung, and the affective responses (tears, feelings of intimacy, ambivalence) to the singing. More than merely reviving vernacular singing traditions, the article argues, this new-found sonic comradery forms not only an affective infrastructure that moves people to tears but also somatic building blocks for national imageries.
The purpose of this article is twofold: first, we show how algorithms have become increasingly central to financial credit scoring; second, we draw on this to further develop the anthropological study of algorithmic governance. As such, we describe the literature on credit scoring and then discuss ethnographic examples from two regulatory and commercial contexts: the US and Denmark. From these empirical cases, we carve out main developments of algorithmic governance in credit scoring and elucidate social and cultural logics behind algorithmic governance tools. Our analytical framework builds on critical algorithm studies and anthropological studies where money and payment infrastructures are viewed as embedded in their specific cultural contexts (Bloch and Parry 1989; Maurer 2015). The comparative analysis shows how algorithmic credit scoring takes different forms hence raising different issues in the two cases. Danish banks seem to have developed a system of intensive, yet hidden credit scoring based on surveillance and harvesting of behavioural data, which, however, due to GDPR takes place in restricted silos. Credit scores are hidden to customers, and therefore there has been virtually no public debate regarding the algorithmic models behind scores. In the US, fewer legal restrictions on data trading combined with both widespread and visible credit scoring has led to the development of a credit data market and widespread use of credit scoring by ‘affiliation’ on the one hand, but also to increasing public and political critique on scoring models on the other.
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