Labor Out of Place: On the Varieties and Valences of (In)visible Labor in Data-Intensive Science (original) (raw)

The Proletarianization of Data Science

Digital Work in the Planetary Market, 2022

Digital Work in the Planetary Market (2022). Edited by Mark Graham and Fabian Ferrari. MIT Press This chapter shows that the data science labour force, while globally distributed, is predominantly tied to powerful firms concentrated in specific locales. In particular, I argue that the planetary data science labour force is increasingly created by and for powerful technology capital in the USA. The increasing efforts of large firms in producing their own bespoke labour force has implications for digital labour in general. From the perspective of capital, data science labour-power is a scarce commodity to be competed for. Around the world, efforts are thus being made to proletarianize data science labour-power; to increase its supply and decrease its value, while capturing a competitive share of it. Wider distribution of the skills to perform currently rewarding and well-remunerated digital labour is often positioned as a means to close the economic gap between the Global North and South, but the distribution of such skills is accompanied by their simplification and consequent devaluation. While the proletarianization of data science labour-power may make more data science jobs available outside the Global North, it will do so only insofar as it reduces the labour costs for big technology firms. Rather than the elevation of less-privileged labourers to the digital era, the proletarianization of data science labour-power suggests the coming degradation of a privileged type of labour to the status of precarious and poorly remunerated ghost work.

Domesticating data: Traveling and value-making in the data economy

Social Studies of Science, 2023

Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and to which data flow. Drawing upon ethnographic fieldwork in two connected data-intensive laboratories and the concept of domestication, we explore what it takes to bring data 'home' into the laboratory. As data come and dwell in the home, they are made to follow rituals, and as a result, data are reshaped and form ties with the laboratory and its practitioners. We identify four main ways of domesticating data. First, through storytelling about the data's origins, data practitioners draw the boundaries of their laboratory. Second, through standardization, staff transform samples into digital data that can travel well while ruling what data can be let into the home. Third, through formatting, data practitioners become familiar with their data and at the same time imprint the data, thus making them belong to their home. Finally, through cultivation, staff turn data into a resource for knowledge production. Through the lens of domestication, we see the data economy as a collection of homes connected by flows, and it is because data are tamed and attached to homes that they become valuable knowledge tools. Such domestication practices also have broad implications for staff, who in the process of 'homing' data, come to belong to the laboratory. To conclude, we reflect on what these domestication processes-which silence unusual behaviours in the data-mean for the knowledge produced in data-intensive research.

Data Cleaners for Pristine Datasets: Visibility and Invisibility of Data Processors in Social Science

2018

Science, Technology, & Human Values, First Published June 2018 This article investigates the work of processors who curate and " clean " the data sets that researchers submit to data archives for archiving and further dissemination. Based on ethnographic fieldwork conducted at the data processing unit of a major US social science data archive, I investigate how these data processors work, under which status, and how they contribute to data sharing. This article presents two main results. First, it contributes to the study of invisible technicians in science by showing that the same procedures can make technical work invisible outside and visible inside the archive, to allow peer review and quality control. Second, this article contributes to the social study of scientific data sharing, by showing that the organization of data processing directly stems from the conception that the

Getting Our Hands Dirty: Reflections on Data

Somatechnics: Special Issue Data Matter, 2019

Data matters, especially empirical findings in the life sciences concerning technologically entangled human and nonhuman forms of embodiment. For example, if we visit a physician, we often have faith in the findings of evidence-based medicine. Many of us follow empirically anchored advice from the life sciences, from health tips to neuroscientific models, from warnings against environmental hazards to cancer therapy, stroke medicine, immunology or organ transplantation. At the same time, we produce data. In the digital age, an increasing number of devices are connected to the Internet and health apps like Ada or Mood Kit collect our data and analyse them, fostering the 'datafication' of our lives (Mayer-Schönberger & Cukier 2013). Whereas data from the life sciences may seem to be politically neutral, or at least more politically neutral than data collected from our online behaviour or app use, a closer observation can reveal problematic aspects, especially in regard to gender. This special issue of Somatechnics aims to tackle key issues about how the production of data is intertwined with matter and with gender in the life sciences, and how theorizing the production of data can be productive both for new materialisms and for gender studies, in particular feminist and queer theories and methodologies. As numerous feminist theoretical interventions since at least the 1970s have shown, matter and bodies have been historically aligned with the realm of 'women' and considered the lesser terms in the matter-thought and in the body-mind binaries, respectively. But, what is data? At first sight-and rooted in everyday practices-the answer seems simple. Data translates selected parts of Somatechnics 9.2-3 (2019): 159-169

Data colonialism through accumulation by dispossession: New metaphors for daily data

Environment and Planning D: Society and Space, 2016

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 ex...

Science as a Vocation in the Era of Big Data: the Philosophy of Science behind Big Data and humanity’s Continued Part in Science

Integrative Psychological and Behavioral Science

We now live in the era of big data, and according to its proponents, big data is poised to change science as we know it. Claims of having no theory and no ideology are made, and there is an assumption that the results of big data are trustworthy because it is considered free from human judgement, which is often considered inextricably linked with human error. These two claims lead to the idea that big data is the source of better scientific knowledge, through more objectivity, more data, and better analysis. In this paper I analyse the philosophy of science behind big data and make the claim that the death of many traditional sciences, and the human scientist, is much exaggerated. The philosophy of science of big data means that there are certain things big data does very well, and some things that it cannot do. I argue that humans will still be needed for mediating and creating theory, and for providing the legitimacy and values science needs as a normative social enterprise.

The value of data: considering the context of production in data economies

2011

In this paper we argue that how scientific collaborations share data is bound up in the ways in which they produce and acquire that data. We draw on ethnographic work with two robotic space exploration teams to show how each community's norms of" data-sharing" are best understood as arising not from the context of the use or exchange of data, but from the context of data production. Shifting our perspective back to the point of production suggests that digital artifacts are embedded in a broader data economy. We present ...

Science as Labor

Perspectives on Science, 2005

The article takes the term "technoscience" literally and investigates a conception of science that takes it not only as practice, but as production in the sense of a material labor process. It will explore in particular the material connection between science and ordinary production. It will furthermore examine how the historical development of science as a social enterprise was shaped by its technoscientiªc character. In this context, in an excursus, the prevailing notion will be questioned that social relations must be conceived of as pure interactions. Finally, the article will go into the relationship between the epistemic dimension of science and its technoscientiªc character.

The data archive as factory: Alienation and resistance of data processors

Big Data & Society, 2021

Archival data processing consists of cleaning and formatting data between the moment a dataset is deposited and its publication on the archive’s website. In this article, I approach data processing by combining scholarship on invisible labor in knowledge infrastructures with a Marxian framework and show the relevance of considering data processing as factory labor. Using this perspective to analyze ethnographic data collected during a six-month participatory observation at a U.S. data archive, I generate a taxonomy of the forms of alienation that data processing generates, but also the types of resistance that processors develop, across four categories: routine, speed, skill, and meaning. This synthetic approach demonstrates, first, that data processing reproduces typical forms of factory worker’s alienation: processors are asked to work along a strict standardized pipeline, at a fast pace, without acquiring substantive skills or having a meaningful involvement in their work. It reveals, second, how data processors resist the alienating nature of this workflow by developing multiple tactics along the same four categories. Seen through this dual lens, data processors are therefore not only invisible workers, but also factory workers who follow and subvert a workflow organized as an assembly line. I conclude by proposing a four-step framework to better value the social contribution of data workers beyond the archive.

Discovering needs for digital capitalism: The hybrid profession of data science

Big Data & Society, 2021

Over the last decade, 'data scientists' have burst into society as a novel expert role. They hold increasing responsibility for generating and analysing digitally captured human experiences. The article considers their professionalization not as a functionally necessary development but as the outcome of classification practices and struggles. The rise of data scientists is examined across their discursive classification in the academic and economic fields in both the USA and Germany. Despite notable differences across these fields and nations, the article identifies two common subjectivation patterns. Firstly, data scientists are constructed as hybrids, who combine generally conflictive roles as both generalists and specialists; technicians and communicators; data exploiters and data ethicists. This finding is interpreted as demonstrating a discursive distinction between data scientists and other competing and supposedly more one-dimensional professionals, such as statisticians or computer scientists. Secondly, the article uncovers a discursive construction that interpellates data scientists as discoverers of needs. They are imagined as explorative work subjects who can establish growth for digital capitalism by generating behavioural patterns that allow for personalization, customization and optimization practices.