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Journal Article

Social Physics, Crowdsourcing and Multicultural Collaborative Research Practice in the Social Sciences: E Pluribus Unum?  pp17-28

David A.L Coldwell

© Apr 2017 Volume 15 Issue 1, Editor: Ann Brown, pp1 - 56

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The possibility of there being an investigator with the knowledge and ability to understand, let alone digest the data embraced by the boundaries of social scientific disciplines seems very remote. However research investigators in the human sciences might in their quest for greater validity and wider generalization in their concepts and theories, adopt a different scientific perspective in addition to its currently most prevalent hypothetico‑deductive approach. The proposed approach made in the paper corresponds more to Baconian‑type methods and those adopted by Darwin in his theory of evolution and involves the use of large data sets of the kind made available by the methodological approaches of social physics and crowdsourcing. The paper suggests that large data sets can be analyzed effectively in multi‑disciplinary, cross‑cultural collaborative research contexts, and a case study illustrating a collaborative research process is described in detail to demonstrate this point. The paper maintains that through the combined utilization of scientific approaches and large data gathering techniques made available through new technologies, it may be possible to generate comprehensively valid empirical and theoretical wholes across the human sciences.


Keywords: Social Physics, crowdsourcing, multicultural, multidisciplinary, collaborative research, social sciences.


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Journal Issue

Volume 15 Issue 1 / Apr 2017  pp1‑56

Editor: Ann Brown

View Contents Download PDF (free)

Keywords: big data, business intelligence, qualitative research method, social media analysis, text mining, text analytics, Social Physics, crowdsourcing, multicultural, multidisciplinary, collaborative research, social sciences, Knowledge Cafés, Theory refinement, Theoretical conjectures, Research Methodology, Hermeneutics, Multiple imputation by chained equations, MICE, missing data, guidelines, review, R


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