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

Michael J. Scroggins, Irene V. Pasquetto

Abstract


We apply the concept of invisible labor, as developed by labor scholars over the last forty years, to data-intensive science. Drawing on a fifteen-year corpus of research into multiple domains of data-intensive science, we use a series of ethnographic vignettes to offer a snapshot of the varieties and valences of labor in data-intensive science. We conceptualize data-intensive science as an evolving field and set of practices and highlight parallels between the labor literature and Science and Technology Studies. Further, we note where data-intensive science intersects and overlaps with broader trends in the 21st century economy. In closing, we argue for further research that takes scientific work and labor as its starting point.


Keywords


data-intensive science; invisible labor

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References


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DOI: https://doi.org/10.17351/ests2020.341



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