Data, Labor, and Power in the Earth Sciences
Recent advances in Big Data and machine learning, while technically innovative, are only the latest in a long string of efforts to transcend the limitations of human reason through the use of quantitative data and mechanical operations. In addition to (sometimes) accelerating the process of discovery, such efforts reorganize epistemic labor and redistribute epistemic authority. Indeed, they depend on such rearrangements for their success. This lecture examines the history of data-centric practices in the Earth sciences, with a focus on the mid-twentieth-century "quantitative revolution" in geomorphology, the study of landforms. It shows how this so-called revolution affected whose epistemic labor was valued and who could speak authoritatively about the processes shaping the Earth’s surface, as well as which objects and processes were considered legitimate subjects of study. While some aspects of this development are specific to the time and discipline in question, others are relevant to today’s adoption of data-centric practices across the sciences.
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