The nonprofit human service sector in the United States is much slower than the private sector in adopting new data technologies to track and improve services, evaluate outcomes, and communicate successes. While for‐profit companies sell data warehouses and analytic services to human service organizations, many organizations lack the resources or administrative commitment to develop data cultures and systems required to foment knowledge production and meaningful data use. Furthermore, documented tensions between key stakeholders, such as funders, managers, frontline staff, and service users, highlight important differences between industry and other sectors in the adoption of data systems. This article draws from interviews and focus groups with many stakeholders and human service organizations to highlight multiple, sometimes conflicting folk theories of data labor in human service organizations. The results demonstrate numerous competing theories for the uses of data and the work of laboring with data in human services. Drawing on these results, we propose a novel competing data values framework for reading data laborers' theories of data and data work, with a horizontal axis spanning from that categorization of poverty of data to information abundance. Our findings indicate that folk theories cluster in specific quadrants of the model, in particular, poverty and extractivism.

中文翻译：

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探索人类服务中数据劳动的民间理论

美国的非营利性人类服务部门在采用新的数据技术来跟踪和改进服务、评估结果和交流成功方面比私营部门慢得多。虽然营利性公司向人类服务组织出售数据仓库和分析服务，但许多组织缺乏资源或管理承诺来开发促进知识生产和有意义数据使用所需的数据文化和系统。此外，关键利益相关者（如资助者、经理、一线工作人员和服务用户）之间记录在案的紧张关系凸显了行业和其他部门在采用数据系统方面的重要差异。本文借鉴了对许多利益相关者和人类服务组织的访谈和焦点小组，重点介绍了人类服务组织中数据劳动的多种有时相互冲突的民间理论。结果展示了许多关于数据使用和在人类服务中处理数据的工作的竞争理论。基于这些结果，我们提出了一个新的竞争数据价值框架，用于阅读数据劳动者的数据理论和数据工作，横轴从数据贫困分类到信息丰富。我们的研究结果表明，民间理论聚集在模型的特定象限中，特别是贫困和采掘主义。