喧嚣之后的沉思——关于社会科学大数据研究的认识论与方法论的讨论

The Contemplation amidst the Hustle and Bustle:Epistemology and Methodology of Big Data Research in Social Sciences

大数据的出现和发展颠覆了传统社会科学研究的思维方式,也引发了一系列哲学层次的讨论。大数据乐观主义者认为,海量数据总是可以产生准确且具实践性的知识,而理论可有可无。为揭示隐匿在大数据分析过程中的问题及其可能产生的后果,笔者将其与传统的社会科学研究方法对比,从数据产生和分析方法两个方面对大数据的认识论和方法论进行探讨。本文认为,数据本身只是一个信息的载体,分析其中潜在的问题与数据的"大"小无关,而是与科学哲学的思维有关。通过大数据分析得出真实而有效的社会知识,需要将知识生产过程建立在适当的科学哲学基础之上,既不可完全依赖理论,亦不可完全抛弃理论。

The emergence and development of big data has subverted the way we think of traditional social science research and triggered a series of philosophical discussions. Big-data optimists argue that massive data can produce more accurate and actionable knowledge even without theory. To reveal the problems hidden in big data analysis,I compare the data generation process and analytical methods of big data with those of traditional survey data from epistemological and methodological perspectives and discuss the potential biases.Results indicate that the main driver of these hidden problems is never about how＂big/small＂the data is,but the philosophy of science. To build effective knowledge about the world,we can neither completely rely on nor completely abandon theory.