ABSTRACT:We focused on young, low-income, African American children in first-to third-grade classrooms where they experienced varied forms of interactive, participatory,
Laboratory technicians are typically portrayed as manual workers following routine procedures to produce scientific data. However, technicians in vertebrate paleontology laboratories often describe their work in terms of creativity and artistry. Fossil specimens undergo extensive preparation--including rock removal, damage repair, and reconstruction of missing parts--to become accessible to researchers. Technicians called 'fossil preparators' choose, apply, and sometimes invent these preparation methods. They have no formal training, no standard protocols, and few publications to consult on techniques. Despite the resulting diversity of people and practices, preparators and their work are usually absent from research publications, making them 'invisible technicians' in Steven Shapin's sense. But preparators reject the view of their work as predictable or simple; in particular, many preparators value art training, the aesthetics of prepared fossils, and the process of creative problem-solving in their work. Based on interviews and participant observation and drawing from literature in science studies, sociology of work, and anthropology of craft, I ask why these technicians compare themselves with artists and how this portrayal affects scientific practice and social order in laboratories. I argue that associating artistry and creativity with their work distances preparators from ideas of unskilled technical work and technicians' low status, thus improving their social role in the laboratory community and preserving their power over laboratory practices.
Who decides what good data science looks like? And who gets to decide what “data ethics” means? The answer is all of us. Good data science should incorporate the perspectives of people who create and work with data, people who study the interactions between science and society, and people whose lives are affected by data science.
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