Sociology between big data and research frontiers, a challenge for educational policies and skills

Stefania Capogna (2022)

Quality and Quantity,
Online first

Abstract. The paper focuses on the challenges posed within sociology and social research by the transformations created by the “data society”. To this end, the paper outlines some of the most significant elements for new frontier research which sociology is forced to confront also in relation to the challenges for educational policies and skills. While leading literature decries the need to promote alphabetising data, otherwise defined as data literacy, the idea proposed here is that it is necessary to work towards understanding data. An understanding that highlights the role sociology has firmly set itself since its foundation, that of studying and explaining the complexity of the relationships that characterize social life in every context and period.

Extract: “Desrosières (2015, 2016) explores the theme by suggesting the need to distinguish between measurement and quantification. In his opinion, quantification comes first and indicates the defining process that leads to the elaboration of univocal, standard concepts, the elaboration of classification and measurement procedures. The defining process develops along a continuous pathway of negotiation, coordination and critical review which involves different actors, belonging to different disciplinary and professional fields and which leads to the construction of a system of shared theoretical-methodological conventions. This means that quantification is anchored to a specific justification logic that legitimises the system of detection and measurement of the phenomenon under consideration. These conventional logics are guided by value principles that direct the different perspectives, through which it is possible to problematise the social world. […]. Considering measurement and quantification in this way allows us to penetrate the black-box of big data, to reflect on: what moves behind the conventions of measurement (Salais 2016); the processes and rationalities underlying the construction of automatic detection systems; the logic of open data; the internal coherence of the theoretical-methodological structure; the methods of data communication and consultation; the limits and approximations that are inevitably connected to the numerical language, just as to the alphabetic language; the values that inform the platform; the stakes of the actors who contribute to the design, implementation, management, supply of the data sets and their related release.” (p. 9-10)

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