从高尔顿的骄傲到杜波依斯的追求:数据驱动的不平等的形式

Colin Koopman
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摘要

数据日益驱动着我们的生活。我们的生活深深沉浸在数据中,这通常被视为一种新的轨迹,它的历史已经超过了一个世纪。通过重新审视今天被称为数据科学的早期先驱的工作,我们可以看到为数据驱动时代提供资金的假设,以及与数据的替代关系。我在这里通过对比两位看似不太可能的早期数据技术专家——弗朗西斯·高尔顿和W.E.B.杜波依斯——来挖掘见解。高尔顿因其对优生学的贡献而闻名,他首先是一位测量技术人员。在许多科学领域,高尔顿的观念仍有相当大的影响,可能没有他对种族不平等的骄傲。在杜波依斯早期的数据研究中,我们可以找到一个更可行、更平等的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From Galton’s Pride to Du Bois’s Pursuit: The Formats of Data-Driven Inequality
Data increasingly drive our lives. Often presented as a new trajectory, the deep immersion of our lives in data has a history that is well over a century old. By revisiting the work of early pioneers of what would today be called data science, we can bring into view both assumptions that fund our data-driven moment as well as alternative relations to data. I here excavate insights by contrasting a seemingly unlikely pair of early data technologists, Francis Galton and W.E.B. Du Bois. Galton, well known for his contributions to eugenics, was first and foremost a tinkering technician of measure. There are numerous domains of science over which Galtonian conceptions retain considerable influence, presumably without his pride in racial inequality. A more viable, because more egalitarian, alternative for the present can be found in the early data work of Du Bois.
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