CHS: Small: Advancing the Human Work of Data Analytics

数据分析，即自动发现大型数据集中的模式，是当代数字实践的一个组成部分。由于它们的规模大、范围广、粒度空前，这些数据是人工难以处理的，因此，数据挖掘算法可以完成人类无法完成的工作。尽管如此，数据分析仍然需要人力来完成，例如决定收集什么数据，预处理数据以使其适合算法，以及理解结果。本研究通过识别、跟踪和分析数据分析实践中涉及的多种形式的人类劳动，并通过使用这种分析来开发数据分析研究和培训的新方法，解决了相关的技术和社会挑战。通过阐明以前主要通过学徒学习的工作实践，这项工作扩展了数据分析的范围，超出了与现有研究人员直接联系的范围。它通过明确数据分析结果的开发方式和开发更好地沟通结果的技术，提高了数据分析的透明度和问责制。它支持在数据分析和领域上下文之间更好地匹配，并展示了集成社会和技术研究的良好实践。这个项目将回答的关键问题是，人和机器如何更有效地协同工作，以理解大规模数据。通过技术社会学家和数据科学家之间的合作，本研究将在分析过程的三个阶段识别和解决无形劳动：(1)概念化：如何将问题概念化并转化为机器可解决的数据分析问题？(2)预处理：如何收集、清理数据，并使其成为算法准备？(3)后处理：数据分析的结果如何被语境化、表示和理解，无论是单独的还是公开的？本研究通过分析数字人文学科对数据分析的吸收来回答这些问题。这是一个揭示人类劳动问题的有用网站，因为数据分析是人文学科的一个强大的潜在工具，但并不直接映射到该领域的传统研究方法。因此，将问题映射到数据分析中，并将数据分析的结果转化为目标领域有意义的论点，需要比“数据原生”学科中更明确的表达。本研究在4个方面对数据分析的实践产生了启示：(1)设计新的数据分析培训课程；(2)开发更好地解决和支持人类劳动的软件系统和研究方法；(3)探索使数据分析过程透明、可问责和可沟通的新方法；(4)建立对数据分析实践的细致入微的社会学理解。

Data analytics, the automatic discovery of patterns in large datasets, is an integral part of contemporary digital practice. Owing to their large scale, broad scope, and unprecedented granularity, such data are manually intractable and, thus, data mining algorithms do work that humans cannot. Still, data analytics necessitates human labor to make it work, for example deciding what data to collect, pre-processing the data to make them algorithm-ready, and making sense of the results. This research addresses related technical and societal challenges by identifying, tracking, and analyzing the multiple forms of human labor involved in the practice of data analytics, and by using this analysis to develop new methods for data analytics research and training. By articulating work practices that previously have been taught largely through apprenticeship, this work expands the reach of data analytics beyond those with direct connections to existing researchers. It increases transparency and accountability of data analysis by making clear how data analysis results are developed and by developing techniques to better communicate results. It supports a better fit between data analysis and domain contexts and demonstrates good practices for integrating social and technical research. The key question this project will answer is how people and machines can work together more effectively to make sense of large-scale data.Through a collaboration between sociologists of technology and data scientists, this research will identify and address invisible labor at three stages in the analytics process: (1) Conceptualization: How is a problem conceptualized and translated into a machine-solvable data analytic problem? (2) Pre-processing: How are data collected, cleaned, and made algorithm-ready? (3) Post-processing: How are the results of data analysis contextualized, represented, and made sense of, both individually and publically? This research answers these questions by analyzing the uptake of data analytics in the digital humanities. This is a useful site for surfacing questions of human labor because data analytics is a powerful potential tool for the humanities, but does not map directly onto traditional research methods in this field. Thus, mapping problems onto data analytics and translating the results of data analytics into meaningful arguments for the target domain requires more explicit articulation than is the case in more "data-native" disciplines. This research develops implications for the practice of data analysis in 4 areas: (1) designing new curricula for training in data analysis; (2) developing software systems and research methods that better address and support human labor; (3) exploring new ways to make the process of data analysis transparent, accountable, and communicable; and (4) creating a nuanced sociological understanding of the practice of data analysis.