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DC Field | Value | Language |
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dc.contributor.author | Bartlett, A | - |
dc.contributor.author | Lewis, J | - |
dc.contributor.author | Reyes-Galindo, L | - |
dc.contributor.author | Stephens, N | - |
dc.date.accessioned | 2018-03-23T10:45:23Z | - |
dc.date.available | 2018-03-23T10:45:23Z | - |
dc.date.issued | 2018-05-08 | - |
dc.identifier.citation | Bartlett, A. et al. (2018) ‘The locus of legitimate interpretation in Big Data sciences: Lessons for computational social science from -omic biology and high-energy physics’, Big Data & Society. doi: 10.1177/2053951718768831. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/16012 | - |
dc.description.abstract | © The Author(s) 2018. Over the past decade, ‘big data’ has been positioned as the indispensable mode of 21st century research across academia (Boyd and Crawford 2012; Kitchin 2014i). While many of the foundational concepts and techniques of the big data sciences were already well-established practices across a number of scientific disciplines, only recently have they been assembled into a distinct field of research claiming legitimacy in and of itself (Kitchin 2014i, 2014ii, Ruppert 2015, Beer 2016, Williams et al. 2017). While social science has a quantitative history with ‘big’ datasets dating back to before Durkheim (1897 [2006]), the emergence of ‘big data’ and computationally-intensive social science is a contemporary phenomenon. As with much of the discourse surrounding big data across the board, there is a tendency to posit the application of ‘big data’ approaches to social science questions as a revolutionary innovation in the profession, both in terms of empirical reach and in theoretical advancement. | en_US |
dc.format.extent | 1 - 15 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | SAGE Publications | - |
dc.rights | Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). | - |
dc.rights.uri | https://www.creativecommons.org/licenses/by-nc/4.0/ | - |
dc.title | The Locus of Legitimate Interpretation in Big Data Sciences: Lessons from -omic biology and high-energy physics | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1177/2053951718768831 | - |
dc.relation.isPartOf | Big Data and Society | - |
pubs.issue | 1 | - |
pubs.publication-status | Published | - |
pubs.volume | 5 | - |
dc.identifier.eissn | 2053-9517 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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