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In this thesis I examine current and future uses of Big Data in archaeology. New technologies have enabled a range of data capture, data storage, and analyses. Digitization in our society has brought new ways of working for archaeologists and the increased amount of data affects how we can understand the world. Big Data reshapes the research process and creates new risks and opportunities for digital archaeology. Several sources have been examined in order to understand what this looks like in archaeology, such as articles published in the journal Internet Archaeology, a questionnaire answered by archaeologists and digital humanists, interviews by two representatives of archaeology, one representative of digital humanities and a chatbot. The archaeological material has become more accessible. Big Data contributes to data-driven science which enforces a paradigm shift. As data is created faster in various ways and to a voluminous extent archaeologists are forced to work with issues related to the management and storage of data. Digital humanists and archaeologists emphasize that Big Data brings efficient working methods and analyses that can contribute with new questions and reliable knowledge. This can increase the relevance of humanities subjects in the research community. That being said, the nature of Big Data often includes quantitative structured data and its result can be characterized as objective and trustworthy. Perceptions of data as bias-free and that Big Data affects theory to become obsolete may jeopardize the use of source criticism. There is a lack of standardization in how to measure and store data in archaeology which contributes to decontextualization of data. I urge that archaeologists should reflect on how cutting edge data-modeling and AI-modeling can assist in the research process as well as how ethical aspects of data should be considered in order not to risk interpretations overlooking people, places and practices.

In this thesis I examine current and future uses of Big Data in archaeology. New technologies have enabled a range of data capture, data storage, and analyses. Digitization in our society has brought new ways of working for archaeologists and the increased amount of data affects how we can understand the world. Big Data reshapes the research process and creates new risks and opportunities for digital archaeology. Several sources have been examined in order to understand what this looks like in archaeology, such as articles published in the journal Internet Archaeology, a questionnaire answered by archaeologists and digital humanists, interviews by two representatives of archaeology, one representative of digital humanities and a chatbot. The archaeological material has become more accessible. Big Data contributes to data-driven science which enforces a paradigm shift. As data is created faster in various ways and to a voluminous extent archaeologists are forced to work with issues related to the management and storage of data. Digital humanists and archaeologists emphasize that Big Data brings efficient working methods and analyses that can contribute with new questions and reliable knowledge. This can increase the relevance of humanities subjects in the research community. That being said, the nature of Big Data often includes quantitative structured data and its result can be characterized as objective and trustworthy. Perceptions of data as bias-free and that Big Data affects theory to become obsolete may jeopardize the use of source criticism. There is a lack of standardization in how to measure and store data in archaeology which contributes to decontextualization of data. I urge that archaeologists should reflect on how cutting edge data-modeling and AI-modeling can assist in the research process as well as how ethical aspects of data should be considered in order not to risk interpretations overlooking people, places and practices.

The global food system is characterized by market concentration and oligopoly. In our article, we focus on the most powerful input supply and machinery companies and analyze how these firms create value, both economic and otherwise, from big data. In digital capitalism, data is valorized across sectors; personal data is aggregated into large-scale datasets, a practice that feeds economic concentration and monopolization. Big data also has become central to the business model for agricultural companies; it is a claim made by the companies themselves. Yet, little is known about their specific strategies to do so. We aim to fill this gap, asking how is agricultural data transformed into value by the most powerful agribusinesses and ag-tech firms? Through the lens of assetization, we examine corporate strategies for transforming agricultural data into value. We draw on literature from food studies, specifically political economic analyses of the historical practices of agricultural corporations, as well as literature from critical data studies that investigates data as an asset. For our analysis, we rely on a variety of gray literature and public-facing documents: financial documents, sustainability and shareholder reports, terms of use, license agreements, and news articles. Our results contribute to the critical data studies literature on agricultural big data by identifying three main strategies of assetization: securing relationships and dependence, price-setting and data sharing, and product development and targeted marketing. The strategies have socio-ecological implications; our results indicate the reproduction of asymmetrical power relations in the agri-food system favoring corporations and the continuation of long-standing dynamics of inequalities., Bundesministerium für Bildung und Forschunghttps://doi.org/10.13039/501100002347, Peer Reviewed

In this article we describe our five-year research project on the notorious radical free speech service and fringe platform Gab. During these years we scraped an entire platform, prepared it into a dataset for analysis, and opened it up to a broader community of students and researchers. Each of these projects provides us not just with a small slice of platformized far-right culture but also with a larger sphere of a fringe platform. However, the overarching goal of the Gab project was to contribute to a methodology for the study of the contemporary platformized far right. The atypical nature of the project posed many methodological, epistemological, and legal challenges. It therefore kicked off an institutional learning process about the possibilities, legal boundaries, and best practices for research compliant with General Data Protection Regulation (GDPR). In this article we argue that the study of the platformized far right should have a thorough understanding of the medium on which the object is present, as well as the methods with which the object is captured. What is more, scholars that use digital tools and data methods for capture and analysis of web platforms must become literate in operating them. Consequently, data-driven research on the far right is naturally interdisciplinary and therefore cooperative and adherent to the principles of open science.

In this article we describe our five-year research project on the notorious radical free speech service and fringe platform Gab. During these years we scraped an entire platform, prepared it into a dataset for analysis, and opened it up to a broader community of students and researchers. Each of these projects provides us not just with a small slice of platformized far-right culture but also with a larger sphere of a fringe platform. However, the overarching goal of the Gab project was to contribute to a methodology for the study of the contemporary platformized far right. The atypical nature of the project posed many methodological, epistemological, and legal challenges. It therefore kicked off an institutional learning process about the possibilities, legal boundaries, and best practices for research compliant with General Data Protection Regulation (GDPR). In this article we argue that the study of the platformized far right should have a thorough understanding of the medium on which the object is present, as well as the methods with which the object is captured. What is more, scholars that use digital tools and data methods for capture and analysis of web platforms must become literate in operating them. Consequently, data-driven research on the far right is naturally interdisciplinary and therefore cooperative and adherent to the principles of open science.

In this special issue, we ask: What do we see when we look at datafied societies through the lens of care? Following the footsteps of feminist writers, activists, and academics who take care as a vantage point for scrutinising and reimaging technoscientific societies, this special issue brings together scholars from critical data studies who explore what we might learn (and see) when we apply care ethics to the study of datafication. To develop a view on datafied societies informed by ethics, concepts, and practices of care, we propose a move from critique to care in social studies of data-driven technologies. We specifically identify five moves in which a care lens provides a new perspective when studying datafication and datafied societies: (1) a move from data-driven technologies to socio-digital care arrangements, (2) a move from data science to data work and care, (3) a move from technical to situated modes of knowledge production, (4) a move from studying harms of datafication to the politics of vulnerability, and (5) a move towards building communities of care. Discussing how critical data studies and care ethics can mutually contribute to each other, this collection explores how this way of thinking can inform new ways of seeing datafied societies and imagine living and being well in more than human worlds nurtured by care.

Feverish narratives of artistic AI-revolution obscure the fact that empirical documentation of the actual impacts of artificial intelligence on artistic practices is still sparse. This paper focuses on the frictions of working with AI artistically. Based on interviews with 20 AI-artists, we 1) demonstrate that frictions experienced with the technological elements of the work processes with AI are inseparably intertwined with the artists’ socio-material realities and the inherent asymmetries of access, and 2) show how frictional ambivalence and unpredictability in artistic interactions with AI tools function both as restrictive and productive elements of the art-making processes, presenting opportunities to reframe the core notions of artistic agency, authorship, and the ontology of art.We discuss these findings in the context of HCI and critical data studies and provide three invitations for designing with and for frictions. Our empirical work contributes to a deeper understanding of the emerging community of AI-artists and invites new mindful perspectives for the design and development of Creative AI applications., QC 20240619

Public discourses have become sensitive to the ethical challenges of big data and artificial intelligence, as scandals about privacy invasion, algorithmic discrimination, and manipulation in digital platforms repeatedly make news headlines. However, it remains largely unexplored how exactly these complex issues are presented to lay audiences and to what extent news reporting—as a window to tech debates—can instil critical data literacy. The present study addresses this research gap and introduces the concept of “data risks”. The main goal is to critically investigate how societal and individual harms of data-driven technology find their way into the public sphere and are discussed there. The empirical part applies a mixed methods design that combines qualitative and automated content analyses for charting data risks in news reporting sampled from prominent English-speaking media outlets of global reach. The resulting inventory of data risks includes privacy invasion/surveillance, data bias/algorithmic discrimination, cybersecurity, and information disorder. The study posits data risks as communication challenges, highlights shortcomings in public discussions about the issue, and provides stimuli for (practical) interventions that aim at elucidating how datafication and automation can have harmful effects on citizens.

In this chapter, I show how the investigation of racialization in datafied applications can be done through an instrumental, epistemological, and ontological approach to datafication and that the results of each approach do not necessarily match. By analyzing the attempted implementation of a tool aimed at measuring the composition of personnel in terms of migration background called Barometer Culturele Diversiteit (BCD) at Utrecht University in the Netherlands, I show how the tool is using ideas about race (instrumental), shaping knowledge through colonial politics (epistemological), and producing race (ontological) simultaneously. Aided by this analysis, I will advocate for an understanding of the use of raceethnic data for affirmative purposes in terms of strategic essentialism, making epistemic imperfection regarding race warranted only in antiracist data systems working toward their own obsolescence.

The current research examines the emergent literature of Critical Data Studies, and particularly aligns with Michael and Lupton’s (2016) manifesto calling for researchers to study the Public Understanding of Big Data. The aim of this paper is to explore Irish stakeholders’ narratives on data sharing in agriculture, and the ways in which their attitudes towards different data sharing governance models reflect their understandings of data, the impact that data hold in their lives and in the farming sector, as well as their preferences for how data should be governed within agriculture. Seven focus groups were held in 2019 with Irish stakeholders from a variety of backgrounds, including agri-researchers, those working in SMEs, and farmers of varying ages and sectors. The primary activities carried out during these focus groups centred upon asking participants to discuss four different data sharing governance models, and to work their way through a set of value cards relating to these models. Focus group results are studied using an inductive, data-driven form of thematic analysis (Braun and Clarke 2006). Five primary themes cross-cut these focus groups: 1) Desire for a data intermediary, 2) Reversing the value chain, 3) Categorisation of data, 4) The common good, and 5) Potential danger in data sharing. These themes are explored in the paper through a detailed discussion of the focus group results, in which the authors track the manifestation of these themes across focus groups, and the ways they sometimes morphed or changed depending upon the participating stakeholder group.

Digital media serve manifold purposes connected to communication, transition, resource allocation, and integration in the context of forced migration. Many apps and platforms rely on user data. Organizations do not always clearly communicate what data are collected for which purposes. Policies on data and privacy should convey this information, but they rarely increase clarity and transparency. This can create vulnerabilities for users and raises questions about data literacy: How much do they understand about data practices and how can organizations inform them better? This qualitative study explores 10 digital services (apps-based and web-based) with the walkthrough method in combination with a content analysis of data policies. It charts communication efforts about data practices and consequences for privacy. The findings imply that many organizations fail to address these issues efficiently. They need to critically revise their communication strategies to create transparency and build data literacy among users.

In the Netherlands, data about the spatial distribution of Dutch citizens structured on the basis of their “migration background” is available in governmental open data sets. In this dissertation, such data are called “race-ethnic” for the combined racial and ethnonationalist connotations of the categorizations and labels. I will argue that the appropriation of race-ethnic data for new purposes enables a process of racialization, understood as the selection of “particular human features for purposes of racial signification” (Omi and Winant 2015, 110). To understand how processes of racialization are not only historically and socially determined, but also result from specific technologies, I propose the concept of technologically mediated racialization (TMR). I use TMR in the investigation of the development of past technologies used for counting and clustering people in the Dutch census between 1899 and 2011. I show how race-ethnic categorization is intimately tied to sociocultural ideas about which human characteristics matter, as well as to the available technologies to record, store, and distribute that information. I will trace the historical connection between contemporary Dutch categories and clusters, such as “person with a migration background”, and older categories and clusters, such as “niet-Westerse allochtoon”, and “vreemdeling”. This will allow me to show that contemporary categorization practices are still informed by Dutch colonial history and migration history. As a result, label changes are both unable to solve racializing practices, and, simultaneously, make historical connections between race-ethnic categories and colonial history harder to access. This process can be understood as a form of colonial aphasia: “a loss of access that may verge on active dissociation” (Stoler 2016, 12). In my first two case studies, I show how race-ethnic categories are made available by the data infrastructure of Statistics Netherlands (CBS) and are used in data app

This chapter focuses on Syrian refugee women as data subjects in the bureaucratic system of the Dutch immigration services (IND). In an increasingly datafied society many aspects of governance are becoming subject to some form of datafication. The same goes for the decision-making process of the immigration services. Recent research on these processes has mainly focussed on data practices by the European Union in order to protect "Fortress Europe" or use by Syrian refugees themselves of social media and telephone. Media and social research on immigration practices has mainly focussed on inequality and the representation of refugees, in society, in policy-making and in the process of integration. This chapter combines a top-down perspective (data system) with a bottom-up perspective (data subjects) on the IND’s data system by integrating an analysis of data and information about Syrian refugee women present in the IND system with the experiences of the women that provided the information. The result is a moving as well as very informative collection of responses, experiences and insights of five Syrian women refugee women who are in, or have been through the IND’s decision-making process and who speak back to the system, producing alternative knowledge and representations to the dominant and mainstream stories of migration and integration in the Netherlands.

This article discusses digitization weaknesses, biases, and malfunctions to challenge popular, almost hegemonic visions of contemporary technologies. By focusing on examples provided by recent mediated cases, controversies, and critical research about biases, we aim to propose an analysis of anything digital starting from its vulnerabilities, to look beyond polarized deterministic views, both optimistic and pessimistic. The article generates from the thematic track: “Weak Systems. Exploring bias, bugs and the vulnerability of digitization” that took place at the VIII STS Italia Conference. The panel brought together scholars from different backgrounds, including STS, history of technology, sociology of communication and critical data and media studies to discuss instances of technological weaknesses in various contexts. The article sums up some of the panel takeaways and pleas for a cooperative and interdisciplinary effort focusing on “weak systems”.

The intensification of ageing of the Belgian population will be a major challenge in the coming years. The current system, where elderly people live in elderly care homes will not be sustainable in this growing context because of the lack of infrastructure and personnel, but also because it is too expensive to maintain it at such large scale. For this reason, the nature of the aid provided to elderly people needs a radically change, with a major shift towards home care (assisted living) as compared to care homes. This trend also responds to the wishes of a majority of people in Belgium which want to grow old at home, connected with their community. This paper presents the first results of an interdisciplinary (sociology/engineering) research project aiming at developing an integrated and secure home care technological solution centred around the elderly and her/his social care network. The solution combines smart home (IoT) solutions together with communication technologies, enabling the cooperation and communication between the care network (family, professional, neighbours…) and the elderly. We will present the analysis of the first qualitative phase of the project aiming at understanding the discrepancies between elderlies’ perceptions of intelligent and social homecare solutions, and perceptions of other members of their care network. This analysis will contribute to the field of critical data studies and ageing sociology by interrogating the perception of digitalisation of care, and its risk of dehumanisation (Lits et al. 2017; Bourguignon et al. 2016), through an original methodology based on crossed interviews from social care network.

In den letzten Jahren ermöglichen immer leistungsstärkere Rechnerkapazitäten neue Formen der Messung, Produktion, Speicherung, Aggregation und Verarbeitung von Daten(mengen) sowie Vernetzung vormals isolierter Datenbestände. Die damit verbundenen Hoffnungen, so ‚bessere‘ Wissensbestände zu generieren, beschäftigen auch das Feld der Stadtforschung und -entwicklung. In Deutschland bündeln sich diese Ambitionen in der nationalen Smart City Charta. Doch wie wurde es möglich, dass im Westlichen Diskurs ‚Wissen‘ oder ‚Information‘ eine derart zentrale Rolle für die Zukunftsfähigkeit von Städten zugewiesen werden konnte? Und was lässt uns denken, dass eine Vernetzung von ‚Information‘ oder ‚Wissen‘ zu ‚besseren‘ Städten beitragen kann? Auf Grundlage genealogischer und diskursanalytischer Ansätze zeichnet diese Arbeit nach, wie die praktische Arbeit an Informations- und Kommunikationstechnologien und das Aufkommen eines bestimmten, kybernetischen Denkstils Anfang des 20. Jh. die Herausbildung eines digitalen Urbanismus mitformte. Fernab der Frage, ob Städte durch datenintensive Steuerungsmethoden tatsächlich ‚besser‘ geplant werden können, beschäftigt sich die Arbeit mit der Art und Weise, wie Stadt im digitalen Urbanismus problematisiert wird. Dies soll die dominante und wichtige Kritik des digitalen Urbanismus als unternehmerisches Projekt dahingehend erweitern, diesen nicht lediglich als geleitet von (falschen) Interessen zu verstehen, sondern stärkeren Fokus auf die Handlungsträgerschaft der eigentlichen technologischen Infrastrukturen zu legen. Eine reale Konsequenz ist, so eine zentrale Hypothese dieser Arbeit, dass historisch gewachsene städtische Probleme im Diskurs des digitalen Urbanismus zunehmend in Informations-/Kommunikationsprobleme umgedeutet werden. Entsprechend können deren Ursachen in einem Informationsdefizit oder mangelnder Vernetzung verortet werden, sodass datenbasierte Lösungen in Form ‚intelligenter‘ Infrastrukturen erstrebenswert erscheinen. Ein so, Developments in computer capacities have sparked variegated future scenarios, often carrying the narrative that with a new data deluge, long pressing planetary problems can finally be solved. Cities too are increasingly imagined and already prepared as the central site for such transformation. Thus, travelling under the label of ‘Smart Cities’ or ‘Digital Cities’ they are thought of as knowledge entities not only by corporations that seek to develop urban markets to place their products and services. Also, the state, engineering sciences and a ‘new urban science’ imagine such technological transformations of current cities by means of ‘intelligent’ infrastructures as a desirable, even necessary form of urban management in order to tackle future urban challenges. A good city hence, is primarily one whose infrastructure enables the seamless transmission of information, and often interchangeably, of its inhabitants’ knowledge. It seems that most pressing urban problems are first and foremost problems of information, communication, or, data. Tracing the genealogy of a cybernetic thought style emerging in the early 20th century and analyzing the policy discourse of digital urbanism in Germany, this thesis asks how that came about and how current problematizations of urban phenomena are potentially affected by it. Acknowledging that technologies are not only tools that execute our intentional practices of problematization but shape these too, this thesis traces the role material infrastructures of information technologies had throughout these processes. Thus, this thesis’ aim is twofold, for one, it seeks to understand how it became possible to ascribe ‘knowledge’ such a central role to city planning, so that current imaginaries of digital urbanism (like Smart Cities) can appear as a sensible mode to address urban challenges. Secondly, on this basis, it is inquired whether such inclination to ‘solve’ urban problems with information technologies may bring with it a side-ef

This article is part of the Global Perspectives, Media and Communication special issue on “Media, Migration, and Nationalism,” guest-edited by Koen Leurs and Tomohisa Hirata. In line with the focus of this issue, we are interested in the ways in which open data is progressively used to construct indicators of the state’s performance in the form of race-ethnic categories. These data initiatives are typical for the ongoing quantification and datafication of society. Through APIs (application programming interfaces), both governmental bodies and third parties are given direct access to data, as well as the ways in which these data are structured. This infrastructure affords the appropriation of statistics concerning the national origins of Dutch citizens for new purposes. Through this data, race-ethnic categories are repurposed to measure the living conditions in the Netherlands, effectively keeping people with non-Dutch roots in the migrant category for up to three generations. To show how this process unfolds in the Netherlands, we investigate two web applications, the Allochtonenmeter and the Leefbaarometer, that make use of race-ethnically constructed data. We will argue that for a more complete understanding of the processes at play in the Dutch “data assemblage,” we need to enrich critical data studies with a postcolonial perspective. In this article, we consider race to be a verb rather than a noun, signifying a process or an action, as this takes away the necessity to communicate a nonessentialist perspective on what is raced, since the object of racing can be different in each new location, situation, and technical context. Our focus is therefore on how human characteristics such as nationality, ethnicity, or class are raced through data-driven processes and in relation to a particular history and culture in the Dutch context. In this light, we find that datafied systems do not merely report on particular groups in society but rather actively produce hierarchi

This dissertation is a theoretical study of the relationship between digitality, knowledge, and power in the age of Big Data. My argument is that human medical research on psychedelic substances supports a critique of what I call "the data episteme." I use "episteme" in the sense developed by philosopher Michel Foucault, where the term describes an apparatus for determining the properties associated with the epistemic condition of scientificity. I write that the data episteme suppresses bodies of knowledge which do not bear the epistemic virtues associated with digital data. These include but are not limited to the capacities for positivistic representation and translation into discrete digital media. Drawing from scientific reports, I demonstrate that certain forms of knowledge regarding the therapeutic mechanisms of psychedelics cannot withstand positivistic representation and digitization. Henceforth, psychedelic research demands frameworks for epistemic legitimation which differ from those predicated on the criteria associated with the data episteme. I additionally claim that psychedelic inebriation promotes a form of thinking which has been called, by various theorists, "negative," "abstract," or "idiosyncratic" thought. Whereas the data episteme denies the existence of negative thought, psychedelic research suggests that this mental function is essential to the successful deposition of psychedelic substances as adjuncts to psychotherapy. For the reasons listed above, psychedelic science provides a uniquely salient lens on the normative operations of the data episteme. In the course of suppressing non-digitizable knowledge, the data episteme implements what Foucault conceptualizes "knowledge-power," a term which affirms the fact that there is no meaningful difference between knowledge and power. Here, "power" may be defined as the power to promote but also to retract conditions on which phenomena may exist across all sites of social, intellectual, and political

The article develops a methodological and empirical approach for gauging the ways Big Data can be collected and distributed through mobile apps. This approach focuses on the infrastructural components that condition the disclosure of smartphone users’ data – namely the permissions that apps request and the third-party corporations they work with. We explore the surveillance ecology of mobile apps and thereby the privacy implications of everyday smartphone use through three analytical perspectives: The first focuses on the ‘appscapes’ of individual smartphone users and investigates the consequences of which and how many mobile apps users download on their phones; the second compares different types of apps in order to study the app ecology and the relationships between app and third-party service providers; and the third focuses on a particular app category and discusses the functional as well as the commercial incentives for permissions and third-party collaborations. Thereby, the article advances an interdisciplinary dialogue between critical data studies, political economy and app studies, and pushes an empirical and critical perspective on mobile communication, app ecologies and data economies.

The article develops a methodological and empirical approach for gauging the ways Big Data can be collected and distributed through mobile apps. This approach focuses on the infrastructural components that condition the disclosure of smartphone users’ data – namely the permissions that apps request and the third-party corporations they work with. We explore the surveillance ecology of mobile apps and thereby the privacy implications of everyday smartphone use through three analytical perspectives: The first focuses on the ‘appscapes’ of individual smartphone users and investigates the consequences of which and how many mobile apps users download on their phones; the second compares different types of apps in order to study the app ecology and the relationships between app and third-party service providers; and the third focuses on a particular app category and discusses the functional as well as the commercial incentives for permissions and third-party collaborations. Thereby, the article advances an interdisciplinary dialogue between critical data studies, political economy and app studies, and pushes an empirical and critical perspective on mobile communication, app ecologies and data economies.

Empirical evidence is critical for democratic policing. Data are paramount for the effective governance of the police. This has become especially clear as police officer-involved homicides have gained national attention in the last half-decade. In the wake of the killing of Michael Brown in 2014, scholars and activists quickly identified and decried the lack of reliable national data on police use of force. Media outlets like The Washington Post intervened, establishing a dataset on all civilians shot and killed by police that has created, for the first time ever, close-to-accurate data on the lethal dangers to civilians from encounters with the police. Yet danger in policing is not just present for civilians that the police encounter. Officers themselves are in danger while carrying out their duties. While many other jobs are statistically more dangerous than policing, including commercial fishing, logging, and roofing, policing is unique in the potential for intentional assault by civilians that officers sometimes face while performing their jobs. Because of this, in the public imagination and in the views of officers themselves, being a police officer is one of the most dangerous jobs in the United States. Many researchers have shown that a preoccupation with danger is a central – if not the central – element of police occupational culture.As officers carry out their various duties as law enforcers, maintainers of order, and social service providers, however, they experience critical incidents from a much wider variety of sources than just intentional violence from civilians. Officers respond to gruesome traffic accidents and crime scenes; they investigate child abuse; they encounter individuals in the worst moments of their lives or who are suffering from severe mental illnesses. All of these encounters create stress that, accumulated over time, can and does place officers at an elevated risk of mental illness (such as PTSD and depression) and can lead to malada

Empirical evidence is critical for democratic policing. Data are paramount for the effective governance of the police. This has become especially clear as police officer-involved homicides have gained national attention in the last half-decade. In the wake of the killing of Michael Brown in 2014, scholars and activists quickly identified and decried the lack of reliable national data on police use of force. Media outlets like The Washington Post intervened, establishing a dataset on all civilians shot and killed by police that has created, for the first time ever, close-to-accurate data on the lethal dangers to civilians from encounters with the police. Yet danger in policing is not just present for civilians that the police encounter. Officers themselves are in danger while carrying out their duties. While many other jobs are statistically more dangerous than policing, including commercial fishing, logging, and roofing, policing is unique in the potential for intentional assault by civilians that officers sometimes face while performing their jobs. Because of this, in the public imagination and in the views of officers themselves, being a police officer is one of the most dangerous jobs in the United States. Many researchers have shown that a preoccupation with danger is a central – if not the central – element of police occupational culture.As officers carry out their various duties as law enforcers, maintainers of order, and social service providers, however, they experience critical incidents from a much wider variety of sources than just intentional violence from civilians. Officers respond to gruesome traffic accidents and crime scenes; they investigate child abuse; they encounter individuals in the worst moments of their lives or who are suffering from severe mental illnesses. All of these encounters create stress that, accumulated over time, can and does place officers at an elevated risk of mental illness (such as PTSD and depression) and can lead to malada

In line with developments in many areas of business and governance, where bureaucracies of all sorts are increasingly datafied for budgetary reasons and the additional possibilities for automated analysis, the Dutch Police started with so-called Intelligence-Led Policing. This development led to the creation of the Crime Anticipation System (CAS). This data-driven system tries to predict crimes with statistics based on three data sources: BVI (Central Crime Database), GBA (Municipal Administration) and CBS (Demographics from Statistics Netherlands). By analyzing the used data categories with a critical data studies approach, we will show that the epistemological question concerning predictive policing systems turns into an ontological one: how are living environments and police work mutually shaped and determined by data? We will argue that intelligence-driven policing is not only a qualitative shift, but also has its continuities, since already existing ideas and biases concerning suspects and crimes are reproduced in the information and system of CAS.

Open government data answers cities' need for new capital resources in a post-recession context and addresses a self-conscious concern about the lagging technological modernization of public institutions. This understanding promotes data-centric management strategies and encourages a mechanistic understanding of how to solve city problems; it leads to new data hybrids born out of data-sharing partnerships between government and the private sector. This proceeding focuses specifically on how, through open data work, staff view their records as administrative and commercial assets and fonts of innovation that improve private and public sector services. Some employees see city records as a source of easy capital, whether cost-saving efficiencies internally or innovation by the private sector. Others see open data as a solution for managing outsized, complex city problems.

Drawing on examples from dating and hookup app cultures, this paper addresses the normative question of how well we are living with data – not only as individuals or selves, but as communities and cultures, through the mundane routines and media practices of everyday life. It outlines and elaborates the original concept of ‘vernacular data cultures’, illustrating the concept through examples from dating sites and apps, and the ways they mediate, shape and are shaped by cultures of gender and sexuality.

Open government data answers cities' need for new capital resources in a post-recession context and addresses a self-conscious concern about the lagging technological modernization of public institutions. This understanding promotes data-centric management strategies and encourages a mechanistic understanding of how to solve city problems; it leads to new data hybrids born out of data-sharing partnerships between government and the private sector. This proceeding focuses specifically on how, through open data work, staff view their records as administrative and commercial assets and fonts of innovation that improve private and public sector services. Some employees see city records as a source of easy capital, whether cost-saving efficiencies internally or innovation by the private sector. Others see open data as a solution for managing outsized, complex city problems.

The use of data and metrics on a professional and personal level has led to considerable discourse around the performative power and politics of 'big data' and data visualization, with academia being no exception. We have developed a university system, ResViz, which publicly visualizes the externally funded research projects of academics, and their internal collaborations. We present an interview study that engages 20 key stakeholders, academics and administrators who are part of the pilot release for the first version of this system. In doing so, we describe and problematize our design space, considering the implications of making metrics visible and their social use within a large organization. Our findings cut across the way people communicate, review and manage performance with metrics. We raise seven design issues in this space practical considerations that expose the tensions in making metrics available for public contestation., QC 20201012

In recent years, much has been written on ‘big data’ in both the popular and academic press. After the hubristic declaration of the ‘end of theory’ more nuanced arguments have emerged, suggesting that increasingly pervasive data collection and quantification may have significant implications for the social sciences, even if the social, scientific, political, and economic agendas behind big data are less new than they are often portrayed. Compared to the boosterish tone of much of its press, academic critiques of big data have been relatively muted, often focusing on the continued importance of more traditional forms of domain knowledge and expertise. Indeed, many academic responses to big data enthusiastically celebrate the availability of new data sources and the potential for new insights and perspectives they may enable. Undermining many of these critiques is a lack of attention to the role of technology in society, particularly with respect to the labor process, the continued extension of labor relations into previously private times and places, and the commoditization of more and more aspects of everyday life. In this article, we parse a variety of big data definitions to argue that it is only when individual datums by the million, billion, or more are linked together algorithmically that ‘big data’ emerges as a commodity. Such decisions do not occur in a vacuum but as part of an asymmetric power relationship in which individuals are dispossessed of the data they generate in their day-to-day lives. We argue that the asymmetry of this data capture process is a means of capitalist ‘accumulation by dispossession’ that colonizes and commodifies everyday life in ways previously impossible. Situating the promises of ‘big data’ within the utopian imaginaries of digital frontierism, we suggest processes of data colonialism are actually unfolding behind these utopic promises. Amid private corporate and academic excitement over new forms of data analysis and visualizatio

In recent years, much has been written on ‘big data’ in both the popular and academic press. After the hubristic declaration of the ‘end of theory’ more nuanced arguments have emerged, suggesting that increasingly pervasive data collection and quantification may have significant implications for the social sciences, even if the social, scientific, political, and economic agendas behind big data are less new than they are often portrayed. Compared to the boosterish tone of much of its press, academic critiques of big data have been relatively muted, often focusing on the continued importance of more traditional forms of domain knowledge and expertise. Indeed, many academic responses to big data enthusiastically celebrate the availability of new data sources and the potential for new insights and perspectives they may enable. Undermining many of these critiques is a lack of attention to the role of technology in society, particularly with respect to the labor process, the continued extension of labor relations into previously private times and places, and the commoditization of more and more aspects of everyday life. In this article, we parse a variety of big data definitions to argue that it is only when individual datums by the million, billion, or more are linked together algorithmically that ‘big data’ emerges as a commodity. Such decisions do not occur in a vacuum but as part of an asymmetric power relationship in which individuals are dispossessed of the data they generate in their day-to-day lives. We argue that the asymmetry of this data capture process is a means of capitalist ‘accumulation by dispossession’ that colonizes and commodifies everyday life in ways previously impossible. Situating the promises of ‘big data’ within the utopian imaginaries of digital frontierism, we suggest processes of data colonialism are actually unfolding behind these utopic promises. Amid private corporate and academic excitement over new forms of data analysis and visualizatio

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project's goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data.