Data Scientists’ Identity Work: Omnivorous Symbolic Boundaries in Skills Acquisition
基于对以色列56位数据科学家、教授和管理者的访谈，研究发现数据科学家通过五种资源构建职业身份，强调杂食性技能获取而非专精，从而提升地位。
Drawing on theories from the sociology of work and the sociology of culture, this article argues that members of nascent technical occupations construct their professional identity and claim status through an omnivorous approach to skills acquisition. Based on a discursive analysis of 56 semi-structured in-depth interviews with data scientists, data science professors and managers in Israel, it was found that data scientists mobilise the following five resources to construct their identity: (1) ability to bridge the gap between scientist’s and engineer’s identities; (2) multiplicity of theories; (3) intensive self-learning; (4) bridging technical and social skills; and (5) acquiring domain knowledge easily. These resources diverge from former generalist-specialist identity tensions described in the literature as they attribute a higher status to the generalist-omnivore and a lower one to the specialist-snob.