数据视觉：学习透过数学抽象看问题
Data Vision: Learning to See Through Algorithmic Abstraction
摘要：
学习看穿数据是当代形式的算法知识生产的核心。虽然经常表现为规则的机械应用，但使算法与数据一起工作需要大量的情境工作。本文探讨了在数据分析学习环境中，机械化和自由裁量权的需求如何表现出来。利用CSCW和社会科学的研究以及两个数据学习环境中的民族志实地调查，我们展示了算法的应用有时被视为规则的机械序列，有时被视为一系列情境决策。铸造数据分析作为一个基于规则的（而不是规则约束）的做法，我们表明，有效的数据视觉需要潜在的分析师跨越正式抽象和经验应急的竞争需求。最后，我们讨论了数据视觉的概念如何帮助更好地利用人类工作在数据分析学习、研究和实践中的作用。
英文摘要：
Learning to see through data is central to contemporary forms of algorithmic knowledge production. While often represented as a mechanical application of rules, making algorithms work with data requires a great deal of situated work. This paper examines how the often-divergent demands of mechanization and discretion manifest in data analytic learning environments. Drawing on research in CSCW and the social sciences, and ethnographic fieldwork in two data learning environments, we show how an algorithm's application is seen sometimes as a mechanical sequence of rules and at other times as an array of situated decisions. Casting data analytics as a rule-based (rather than rule-bound) practice, we show that effective data vision requires would-be analysts to straddle the competing demands of formal abstraction and empirical contingency. We conclude by discussing how the notion of data vision can help better leverage the role of human work in data analytic learning, research, and practice.
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