大数据时代的社会研究:新数据世界的挑战和社会研究社会学的需要

Rainer Diaz-Bone, Kenneth Horvath, V. Cappel
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引用次数: 6

摘要

:»大数据社会研究。《社会科学与社会发展》和《社会科学与社会发展》。大数据现象不仅深刻影响着当今社会,也对社会研究提出了严峻的挑战。本文主张向社会研究的社会学方向发展,以描述大数据的新特性及其不足。我们借鉴惯例经济学(EC)的新实用主义方法作为这种社会学视角的概念基础。该框架建议调查量化过程与论证顺序和评估逻辑的相互作用。因此,方法论问题,如“大数据的质量”问题,必须在其与认知价值、制度形式和历史背景的深刻纠缠中进行讨论,并必然隐含政治问题,如谁控制和有权访问数据基础设施。在此概念基础上,文章以健康为例,讨论大数据分析对社会研究的挑战。新的大规模私有数据基础设施的兴起,对大量关联数据的经济评估,或者“量化自我”的运动,这些现象都表明,与现有的社会研究形式相比,这是一种深刻的转变。方法论和认识论,以及制度和政治上的战略,都面临着被大数据分析“超越”和“取代”的风险,就像美国和中国的大型互联网企业已经做过的那样。总之,我们认为概述的发展对大数据时代的研究实践和方法教学都具有重要意义。
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Social Research in Times of Big Data: The Challenges of New Data Worlds and the Need for a Sociology of Social Research
: »Sozialforschung in den Zeiten von Big Data. Die Herausforderungen der neuen Datenwelten und die Notwendigkeit einer Soziologie der Sozialfor-schung« . The phenomenon of big data does not only deeply affect current soci-eties but also poses crucial challenges to social research. This article argues for moving towards a sociology of social research in order to characterize the new qualities of big data and its deficiencies. We draw on the neopragmatist approach of economics of convention (EC) as a conceptual basis for such a sociological perspective. This framework suggests investigating processes of quantification in their interplay with orders of justifications and logics of evaluation. Methodological issues such as the question of the “quality of big data” must accordingly be discussed in their deep entanglement with epistemic values, institutional forms, and historical contexts and as necessarily implying political issues such as who controls and has access to data infrastructures. On this conceptual basis, the article uses the example of health to discuss the challenges of big data analysis for social research. Phenomena such as the rise of new and massive privately owned data infrastructures, the economic valuation of huge amounts of connected data, or the movement of “quantified self” are presented as indications of a profound transformation compared to established forms of doing social research. Methodological and epistemological, but also institutional and political, strategies are presented to face the risk of being “outper-formed” and “replaced” by big data analysis as they are already done in big US American and Chinese Internet enterprises. In conclusion, we argue that the sketched developments have important implications both for research practices and methods teaching in the era of big data.
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