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While most of the existing research about online health information focuses exclusively on either the provider or the user side of communication circuits, this article aims to integrate and discuss both sides and their mediated relation to one another. Drawing on actor-network theory, it conceptualizes the provision and use of online health information as sociotechnical. It questions concretely how website providers position their websites and information, how users browse through the web and assemble information, and interrogates the various concepts of online health information these different practices imply. Further, it asks how search engines, and Google in particular, come to play such a dominant role in the way health-related web information is provided and used. The article concludes by evaluating the implications of the findings in regard to debates about the quality of online health information and the way in which web information is distributed and acquired on a broader scale. (7) 1124 INTRODUCTION Medical information has been described as no longer being bound to medical institutions, but as having 'escaped' into society at large by means of media, most notably new media (Nettleton, 2004). The internet in particular has become an important location where health-related information is circulated and accessed today. Consequently, the web as a health information source has been placed at the centre of the social scientific research agenda. A remarkable body of research has been concentrating on the role that online health information plays in medical practices. Apart from a few critical voices (Henwood et al., 2003) the web has widely been described as a tool of empowerment that strengthens patients (Fox and Rainie, 2000;Hardey, 1999). However, controversy has arisen regarding discussions of the diversity and quality of the information available online. While some scholars celebrate the plurality of the information, ranging from expert to non-expert knowledge (Hardey, 1999;Loader et al., 2002;Nettleton, 2004), others cast into doubt its quality against the background of medical criteria, formulating warnings against misinformation . As a solution to the problem, medical professionals and policymakers alike have made demands for standardized quality labelling such as 'Health on the Net' (HON, see www.hon.ch; European Commission, 2002;. These labels certify websites as trustworthy when corresponding to medically defined standards and hence try to direct users to the 'right' information. However, these quality labels are hardly effective when it comes down to the actions of the end-user, as a number of studies have shown (Eysenbach and Köhler, 2002).One reason for this trend is that the idea of qualifying the source of a website does not correspond well with practices of using the web. Users have their own strategies for searching, selecting and evaluating health information and these are closely related to the way that they usually browse the web (Adams et al., 2006). This indicates that the focu...

As of 2020, the Public Employment Service Austria (AMS) makes use of algorithmic profiling of job seekers to increase the efficiency of its counseling process and the effectiveness of active labor market programs. Based on a statistical model of job seekers' prospects on the labor market, the system—that has become known as the AMS algorithm—is designed to classify clients of the AMS into three categories: those with high chances to find a job within half a year, those with mediocre prospects on the job market, and those clients with a bad outlook of employment in the next 2 years. Depending on the category a particular job seeker is classified under, they will be offered differing support in (re)entering the labor market. Based in science and technology studies, critical data studies and research on fairness, accountability and transparency of algorithmic systems, this paper examines the inherent politics of the AMS algorithm. An in-depth analysis of relevant technical documentation and policy documents investigates crucial conceptual, technical, and social implications of the system. The analysis shows how the design of the algorithm is influenced by technical affordances, but also by social values, norms, and goals. A discussion of the tensions, challenges and possible biases that the system entails calls into question the objectivity and neutrality of data claims and of high hopes pinned on evidence-based decision-making. In this way, the paper sheds light on the coproduction of (semi)automated managerial practices in employment agencies and the framing of unemployment under austerity politics.