No profit e impatto sociale al tempo dei Big Data: tra Quantified Context e Blockchain

Titolo Rivista SALUTE E SOCIETÀ
Autori/Curatori Antonio Maturo, Marta Gibin
Anno di pubblicazione 2020 Fascicolo 2020/1
Lingua Italiano Numero pagine 18 P. 157-174 Dimensione file 199 KB
DOI 10.3280/SES2020-001012
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FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

The aim of this paper is to analyse the opportunities that digitalization and big data can offer for non-profit organizations, and in particular the role they can play in the evaluation of social impact. Big data are a valuable resource for the quantification of the social context, that represents a strong basis to both design policies which are evidence-based, and to measure the impact of the actions implemented. Blockchain technology is an example that shows how digitalization can support this process, and it has been widely used by philanthropic organizations to guarantee the transparency of their projects and receive outcome-based donations. While in the healthcare system big data has led to a Quantified Self, the analysis of "social" big data might lead to an increased importance of what can be called Quantified Context.

Parole chiave:big data; evidence-based policy; quantified context; blockchain; impatto sociale; no profit.

  1. Halford S., Pope C., Weal M. (2013). Digital futures? Sociological challenges and opportunities in the emergent semantic web. Sociology, 47(1): 173-189. DOI: 10.1177/003803851245379
  2. Halford S., Savage M. (2017). Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research. Sociology, 51(6): 1132-1148. DOI: 10.1177/003803851769863
  3. Hey T., Tansley S., Tolle K. (2009). Jim Grey on eScience: A transformed scientific method. In: Hey T., Tansley S., Tolle K., editors, The Fourth Paradigm: Data-Intensive Scientific Discovery. Redmond: Microsoft Research.
  4. Ito J., Narula N., Ali R. (2017). The Blockchain Will Do to the Financial System What the Internet Did to Media. Harvard Business Review, 8 marzo 2017. -- Testo disponibile al sito: https://hbr.org/2017/03/the-blockchain-will-do-to-banks-and-law-firms-what-the-internet-did-to-media (26/07/2019).
  5. Katz J.E. (2008). Handbook of mobile communication studies. London: The MIT Press.
  6. Kelling S., Hochachka W., Fink D., Riedewald M., Caruana R., Ballard G., Hooker G. (2009). Data-intensive Science: A new paradigm for biodiversity studies. BioScience, 59(7): 613-620.
  7. Kitchin R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, April-June 2014: 1-12. DOI: 10.1177/205395171452848
  8. Laidin L., Papadopoulou K.A., Dane N.A. (2019). Parameters for Building Sustainable Blockchain Application Initiatives. The JBBA, 2(1): 1-6.
  9. Lombi L. (2015). La ricerca sociale al tempo dei big data: sfide e prospettive. Studi di Sociologia, 2: 215-227. DOI: 10.1400/23381
  10. Luhmann N. (1990). Sistemi sociali. Bologna: il Mulino.
  11. Mandiberg M. (2012). The social media reader. New York: NYU Press, New York University.
  12. Manovich L. (2012). Trending: The promises and the challenges of big social data. In: Gold M.K., Debates in the Digital Humanities. Minneapolis, MN: University of Minnesota Press.
  13. Marres N., Gerlitz C. (2016). Interface methods: Renegotiating the relations between digital social research, STS and the sociology of innovation. Sociological Review, 64: 21-46. DOI: 10.1111/1467-954X.1231
  14. Mattila J. (2016). “The Blockchain Phenomenon – The Disruptive Potential of Distributed Consensus Architectures”, ETLA Working Papers No 38. Testo disponibile al sito: http://pub.etla.fi/ETLA-Working-Papers-38.pdf (25/07/2019).
  15. Maturo A., Moretti V. (2019). Digital Health and the Gamification of Life. New York: Emerald.
  16. Montero A.L. (2015). Evidence in public social services: and overview from practice and applied research. Bruxelles: The European Social Network.
  17. Olshannikova E., Olsson T., Huhtamäki J., Kärkkäinen H. (2017). Conceptualizing Big Social Data. Journal of Big Data, 4(3): 1-19.
  18. Parkhurst J. (2017). The Politics of Evidence. From evidence-based policy to the good governance of evidence. London – New York City: Routledge.
  19. Porway J. (2015). Five principles for applying data science for social good. O’Reilly, 1° ottobre 2015. -- Testo disponibile al sito: https://www.oreilly.com/ideas/five-principles-for-applying-data-science-for-social-good (14/07/2019).
  20. Rogers R. (2013). Digital Methods. Cambridge, MA: The MIT Press.
  21. Savage M., Burrows R. (2007). The coming crisis of empirical sociology. Sociology, 41(5): 885-899. DOI: 10.1177/003803850708044
  22. Spaggiari O. (2013). Big Data: i numeri che ci migliorano la vita. Vita, 22 novembre 2013. Testo disponibile al sito: http://www.vita.it/it/article/2013/11/22/big-data-i-numeri-che-ci-migliorano-la-vita/125360/ (26/07/2019).
  23. Tinati R., Halford S., Carr L., Pope C. (2014). Big Data: Methodological Challenges and Approaches for Sociological Analysis. Sociology, 48(4): 663-681. DOI: 10.1177/003803851351156
  24. Wesselink A., Colebatch H., Pearce W. (2014). Evidence and policies: discourses, meanings and practices. Policy Sciences, 47(4): 339-344.
  25. Zumbrun J. (2014). SAT Scores and Income Inequality: How Wealthier Kids Rank Higher. The Wall Street Journal, 7 Ottobre 2014. -- Testo disponibile al sito: https://blogs.wsj.com/economics/2014/10/07/sat-scores-and-income-inequality-how-wealthier-kids-rank-higher/ (28/07/2019).
  26. Al-Saqaf W., Seidler N. (2017). Blockchain technology for social impact: opportunities and challenges ahead. Journal of Cyber Policy, 2(3): 338-354. DOI: 10.1080/23738871.2017.140008
  27. Anderson C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired, 23 giugno 2008. -- Testo disponibile al sito: https://www.wired.com/2008/06/pb-theory/ (26/07/2019).
  28. Bellini M. (2019). Blockchain: cos’è, come funziona e gli ambiti applicativi in Italia. Blockchain4Innovation, 18 giugno 2019. -- Testo disponibile al sito: https://www.blockchain4innovation.it/esperti/blockchain-perche-e-cosi-importante/#Smart_Contract_e_blockchain (26/07/2019).
  29. Black N. (2001). Evidence based policy: Proceed with care. British Medical Journal, 323: 275-279.
  30. Blind K. (2018). “Current Challenges for Measuring Innovation, their Implications for Evidence-Based Innovation Policy and the Opportunities of Big Data”, paper presentato alla Conferenza “Impact of R&I Policy at the Crossroads of Policy Design, Implementation, Evaluation”, Vienna, 5 Novembre 2018. -- Testo disponibile al sito: https://www.impactevaluation.eu/wp-content/uploads/2018/11/Blind_etal_Assessing-impacts-1.pdf (26/07/2019).
  31. boyd d., Crawford K. (2011). “Six Provocations for Big Data”, presentato all’Oxford Internet Institute, A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, 21 Settembre. -- Testo disponibile al sito: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431 (26/07/2019).
  32. Brooks D. (2013). What data can’t do. New York Times, 18 February 2013. -- Testo disponibile al sito: https://www.nytimes.com/2013/02/19/opinion/brooks-what-data-cant-do.html (14/07/2019).
  33. Burrows R., Savage M. (2014). After the crisis? Big Data and the methodological challenges of empirical sociology. Big Data & Society, 1(1): 1-6. DOI: 10.1177/205395171454028
  34. Chetty R., Hendren N., Kline P., Saez E. (2015). Economic mobility. Pathways, Special Issue 2015 – State of the States: The Poverty and Inequality Report, pp. 55-60.
  35. Crompton R. (2008). 40 years of sociology: Some comments. Sociology, 42(6): 1218-1227. DOI: 10.1177/003803850809694
  36. Desouza K.C., Smith K.L. (2014). Big Data for Social Innovation. Stanford Social Innovation Review, Summer 2014: 39-43.
  37. DiMaggio P. (2015). Adapting computational text analysis to social science (and vice versa). Big Data & Society, July-December: 1-5. DOI: 10.1177/205395171560290
  38. Frade C. (2016). Social theory and the politics of big data and method. Sociology, 50(5): 863-877. DOI: 10.1177/003803851561418
  39. Galen D.J., Brand N., Boucherle L., Davis R., Do N., El-Baz B., Kimura I., Wharton K., Lee J. (2018). Blockchain for Social Impact: Moving Beyond the Hype. Stanford: Stanford Graduate School of Businness, Center for Social innovation, RippleWorks.
  40. Gandomi A., Haider M. (2015). Beyond the hype: Big data concepts, methods and analytics. International Journal of Information Management, 35: 137-144.
  41. gartner.com (2013). Big data. -- Testo disponibile al sito: https://www.gartner.com/it-glossary/big-data/ (11 /07/2019).
  42. Goldthorpe J. (2016). Sociology as a Population Science. Cambridge: Cambridge University Press.

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Antonio Maturo, Marta Gibin, No profit e impatto sociale al tempo dei Big Data: tra Quantified Context e Blockchain in "SALUTE E SOCIETÀ" 1/2020, pp 157-174, DOI: 10.3280/SES2020-001012