Sensitizing social data science: Combining empirical social research with computational approaches to the analysis of career data

Gian Campagnolo, Robin Williams, Beatrice Alex, Alberto Acerbi, Duncan Chapple

Research output: Working paper

Abstract

How can social science contribute to data science? The paper responds by presenting a new model for disciplinary integration based on conceiving subjects and objects of the research as well as methods as co-participants in shaping research design and questions. Identified with the notion of participative epistemology, this model is exemplified through the presentation of an on-going social data science project combining ethnographic, computational and inferential approaches to analyse career data of thousands IT industry analysts. By focusing on the project’s research design and pilot application of a descriptive approach to test the framework in preparation to the full-scale analysis, we contribute to digital sociology debate by showing how social science - and the sociology of scientific knowledge in particular - can sensitise data science from within, thus surpassing divisive attempts as well as scholarship that conceives social science as offering an ‘external’ contribution to data science.
Original languageEnglish
Pages1-49
Number of pages49
DOIs
Publication statusIn preparation - 2017

Keywords

  • social data science
  • career studies
  • participative epistemology
  • Sequence Analysis

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