Большие данные, новые эпистемологии и смена парадигм / Пер. с англ. О. Н. Шаевой
Аннотация
Рассматриваются вопросы: как доступность больших данных в сочетании с новыми подходами к анализу данных ставят под сомнение традиционную эпистемологию применительно к естественным, социальным и гуманитарным наукам? В какой степени данные изменения выступают источником смены парадигм в различных научных дисциплинах? В частности, критически проанализированы новые формы эмпиризма, в рамках которых провозглашаются «конец теории», развитие науки, «идущей от данных», а не «от знания», а также цифровых гуманитарных и вычислительных социальных наук, предлагающих качественно новые способы осмысления культуры, истории, экономики и общества. Показано, во-первых, что большие данные и новые подходы к анализу данных представляют собой инновации, во многих отношениях перестраивающие привычный порядок проведения научного исследования. Во-вторых, существует острая необходимость в масштабном критическом осмыслении научным сообществом последствий разворачивающейся революции данных для эпистемологии. Едва ли можно отметить попытки решения данной задачи, несмотря на бурные изменения, происходящие в исследовательской практике. На основе критического обзора возникающих в настоящее время эпистемологических оснований науки утверждается, что потенциально продуктивным подходом должно стать развитие эпистемологии, учитывающей специфику рассматриваемого явления в его контексте
Ключевые слова:
большие данные, анализ данных, эпистемология, парадигмы, конец теории, наука, «идущая от данных», цифровые гуманитарные науки, вычислительные социальные науки
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Moretti F. Graphs, Maps, Trees: Abstract Models for a Literary History. London: Verso, 2005.
Open Data Center Alliance. Big Data Consumer Guide. Open Data Center Alliance. 2012. URL: http://www.opendatacenteralliance.org/docs/Big_Data_Con¬sumer_Guide_Rev1.0.pdf (data of access: 11.02.2013).
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Porway J. You Can’t Just Hack Your Way to Social Change // Harvard Busi¬ness Review Blog. 2013. 7 March. URL: http://blogs.hbr.org/cs/2013/03/you_cant_ just_hack_your_way_to.html (data of access: 09.03.2013).
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Ramsay S. Reading Machines: Towards an Algorithmic Criticism. Champaign: University of Illinois Press, 2010.
Ribes D., Jackson S.J. Data Bite Man: The Work of Sustaining Long-term Study // ‘Raw Data’ is an Oxymoron / Ed.: L. Gitelman. Cambridge, MA: MIT Press, 2013. P. 147–166.
Rogers S. Twitter’s Languages of New York Mapped // The Guardian. 2013. 21 February. URL: http://www.guardian.co.uk/news/datablog/interactive/2013/feb/21/ twitter-languages-new-york-mapped (data of access: 03.04.2013).
Rose G. Situating Knowledges: Positionality, Reflexivities and Other Tactics // Progress in Human Geography. 1997. Vol. 21. Is. 3. P. 305–320.
Ruppert E. Rethinking Empirical Social Sciences // Dialogues in Human Geography. 2013. Vol. 3. Is. 3. P. 268–273.
Schnapp J., Presner P. Digital Humanities Manifesto 2.0. 2009. URL: http:// www.humanitiesblast.com/manifesto/Manifesto_V2.pdf (data of access: 13.03.2013).
Seni G., Elder J. Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. San Rafael: Morgan and Claypool, 2010.
Siegel E. Predictive Analytics. Hoboken: Wiley, 2013.
Steadman I. Big Data and the Death of the Theorist // Wired. 2013. 25 Janu¬ary. URL: http://www.wired.co.uk/news/archive/2013-01/25/big-data-end-of-theory (data of access: 30.01.2013).
Strasser B.J. Data-driven Sciences: From Wonder Cabinets to Electronic Databases // Studies in History and Philosophy of Biological and Biomedical Sciences. 2012. Vol. 43. P. 85–87.
Strom D. Big Data Makes Things Better // Slashdot. 2012. 3 August. URL: http://slashdot.org/topic/bi/bigdata-makes-things-better/ (data of access: 24.10.2013).
Trumpener K. Critical Response I. Paratext and Genre System: A Response to Franco Moretti // Critical Inquiry. 2009. Vol. 36. Is. 1. P. 159–171.
Wyly E. Automated (Post)positivism. Urban Geography. (In press)
Zikopoulos P. C., Eaton C., DeRoos D., et al. Understanding Big Data. New York: McGraw Hill, 2012.
