Participative epistemology in social data science: Combining ethnography with computational and statistical approaches

Gian Marco Campagnolo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, I introduce the notion of participative epistemology and discuss how it can contribute to make social data science more accountable. I do so by offering the case of a project where ethnographic, computational and sequence analysis methods have been used in combination. By presenting here in greater detail research design and pilot results of a project using professional networking data to understand the careers of IT industry analysts, I suggest a view on the collaboration between data science and social science as coordinated labour. The application of participative epistemology to social data science is articulated in three points: (1) a more tactical view on the partnerships with commercial data where shared value system is not a pre-requisite for coordinated knowledge production; (2) an appreciation for complementarities in perspective between phenomenological sociology, expertise in computer science associated to digitalisation and the narrative positivism linked with the use of statistics and (3) a view on social data science as contributing empirical sociology with new sensitizing concepts, taking ethnography to reflectively address its own presuppositions.
Original languageEnglish
Pages (from-to)391-403
JournalInternational Journal of Social Research Methodology
Volume25
Issue number3
Early online date1 Mar 2021
DOIs
Publication statusPublished - 4 May 2022

Keywords

  • social data science
  • ethnography
  • interdisciplinarity
  • sociology of professions

Fingerprint

Dive into the research topics of 'Participative epistemology in social data science: Combining ethnography with computational and statistical approaches'. Together they form a unique fingerprint.

Cite this