The ideological problematic of (big) data and Social Informatics

The ideological problematic of (big) data and Social

InformaticsAn analytic discussion and one example

Michael Marcinkowski : [email protected]

“To identify the social impacts of computing, one must have, at least implicitly, a theory of the causal powers that computerized systems can exert upon individuals, groups, organizations, institutions, social networks, social worlds, and other social entities” [4, p. 62].


“[T]he empirical objects of social informatics can be as much

conceptual constructs as empirical entities” [1, p. 576].



Empirical Critical


Empirical Critical

(Big) Data


Empirical Critical

(Big) Data (Big) Data (Movements of computerization

and dataization [5])


Empirical Critical

(Big) Data (Big) Data (Movements of computerization

and dataization [5])(Hyper-theoretical and a-

theoretical [3])

Computerization Movements


Computerization Movements

Dataization Movements


Empiricism


Subject ObjectData

“Peak Empiricism” [6]


Subject Object

Data

Ambient Literature“[W]hat might happen when data aspires to literary form” [2, p. 140].


References1. Day, R. E. (2007). Kling and the “critical”: Social informatics and critical informatics. Journal of the American Society

for Information Science and Technology, 58(4), 575–582. https://doi.org/10.1002/asi.20546

2. Dovey, J. (2016). Ambient Literature: Writing Probability. In U. Ekman, J. D. Bolter, L. Diaz, M. Søndergaard, & M. Engberg (Eds.), Ubiquitous Computing, Complexity and Culture (pp. 141–154). New York, NY: Routledge.

3. Fonseca, F., & Marcinkowski, M (2016). Has (big) data already won? In: International Symposium on Science of Science, 22-23 March 2016, Library of Congress, Washington DC, USA.

4. Kling, R. (1980). Social analyses of computing: theoretical perspectives in recent empirical research. ACM Computing Surveys, 12(1), 61–110. https://doi.org/10.1145/356802.356806

5. Marcinkowski, M. (2016). Data, ideology, and the developing critical program of social informatics. Journal of the Association for Information Science and Technology, 67(5), 1266–1275. https://doi.org/10.1002/asi.23483

6. Marcinkowski, M., & Fonseca, F. (2016). The conditions of peak empiricism in big data and interaction design. Journal of the Association for Information Science and Technology, 67(6), 1279–1288. https://doi.org/10.1002/asi.23497


https://doi.org/10.1002/asi.20546

https://doi.org/10.1145/356802.356806


