Big Data Economies and Ecologies
Big Data Economies and Ecologies
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大数据经济与生态
DOI：
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发表时间：
2015
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通讯作者：
E. Ruppert
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作者：
E. Ruppert
由数字活动、移动的电话使用、交易、众包、数字化等产生的大数据沿着数据链接、挖掘、关联和可视化等创新分析模式正在重新配置社会科学方法和知识实践。社会科学家现在不仅仅是生成数据，他们还收集、收集、组装和重新利用许多数字设备生成的数据，这些设备通常不是他们制造的。然而，社会科学家长期以来一直在重复使用文献档案中汇编的数据或人口普查和调查产生的数据。因此，问题不在于数据的再利用，而在于这些新形式的数据所调动的知识实践的认识论和本体论影响。我通过思考主体、所有者、中介者、翻译者和看门人之间的关系、中继、依赖和投资的转变来研究这些影响，这些关系、中继、依赖和投资构成了大数据的经济和生态。然而，由于这些复杂的关系越来越多地掩盖或使数据的出处无法访问，我提请注意我们如何思考什么方法制定和它们提升的现实种类。我称之为“决定性的数据”，这一举措理解大数据的“现实”是在其动员效应中找到的，也就是说，当被方法采用时，它将实现和合法化的世界。这是一种不会远离但更接近大数据的方法，不是一种反数据主义，而是一种新的数据主义，它不会后退和批评事实，而是更接近它们作为关注的问题。我认为，一个道德挑战是找到对我们调动大数据的方法的影响负责，负责和负责的方法，以及它们提升和促进的世界和方式。
Big Data generated by digital activities, mobile phone use, transactions, crowdsourcing, digitisation and so on along with innovative modes of analysis such as data linking, mining, correlating, and visualising are reconfiguring social science methods and knowledge practices. Rather than only generating data, social scientists now also scrape, harvest, assemble and re-purpose data generated by numerous digital devices often not of their making. Yet social scientists have long re-used data such as that compiled in documentary archives or generated by censuses and surveys. At issue then is not so much the re-purposing of data but the epistemological and ontological effects of knowledge practices that these new forms of data are mobilising. I examine these effects by thinking about the shifting relations, relays, dependencies and investments between subjects, owners, mediators, translators and gatekeepers that make up the economies and ecologies of Big Data. However, because these complex relations increasingly bury or make the provenance of data inaccessible, I bring attention to how we might think about what methods enact and the kinds of realities that they elevate. I call this ‘decisive data’, a move that understands that the 'reality' of Big Data is to be found in its mobilising effects, that is, worlds that it comes to actualise and legitimise when taken up by methods. It is an approach that doesn’t get further away from but closer to Big Data, not an anti-empiricism but a renewed empiricism that does not stand back and critique facts but gets closer to them as matters of concern. I argue that one ethical challenge is to find ways of being accountable, answerable and responsible to the effects of our methods that mobilise Big Data and the worlds and ways of being they elevate and promote.