The sociological study of knowledge infrastructures and classification has traditionally focused on the politics and practices of classifying things or people. However, actors’ work to escape dominant infrastructures and pre-established classification systems has received little attention. In response to this, this article argues that it is crucial to analyze, not only the practices and politics of classification, but also actors’work to escape dominant classification systems. The article has two aims: First, to make a theoretical contribution to the study of classification (...) by proposing to pay analytical attention to practices of escaping classification, what the article dubsclassification egress. This concept directs our attention not only to the practices and politics of classifying things, but also to how actors work to escape orresist classification systemsin practice. Second, the article aims to increase our understanding of the history of quantified and statistical health surveillance. In this, the article investigates how actors in health surveillance assembled a knowledge infrastructure for surveilling, quantifying, and detecting unknown patterns of congenital malformations in the wake of the thalidomide disaster in the early 1960s. The empirical account centers on the actors’ work to detect congenital malformations and escape the dominant nosological classification of diseases, the International Classification of Diseases (ICD), by replacing it with a procedural standard for reporting of symptoms. Thus, the article investigates how actors deal with the tension between the-already-known-and-classified and the unknown-unclassified-phenomenon in health surveillance practice. (shrink)

The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis (...) with predictive analytics, and how this conceptually contradicts the collective basis of insurance. The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling. The third part attempts to assess the extent of this shift in motor insurance. Onboard devices that collect continuous driving behavioural data could import this new paradigm into these products. An examination of the current state of research on models with telematics data shows however that the epistemological leap, for now, has not happened. (shrink)