A Multi-Level Structural Approach Towards Canon-Formation
ABSTRACT | The research aims at contributing to the discourse on the dynamics and structural properties of canon-formation in the Western Artworld. Taking museum collections as condensed forms of local canons, it aims to build network representations of micro-, meso- and macro-level interactions underlying the structure of these collections. Better understanding the network backbone of museum collections may give us novel perspective to tackle subtle mechanisms underlying canon-formation. Besides showing the approach and results, the presentation will also make transparent the assumptions based on which data was handled and will show dataset related biases. It will do so in order to show that neglected biases may reinforce the power structure based on which the dataset biases came to existence in the first place, and to raise epistemological questions related to big data usage.
BIO | Júlia Perczel is a PhD candidate at the Department of Network and Data Science at CEU. Holding an MA in Art History and in Social and Organizational Psychology, she is interested in integrating qualitative and quantitative methodology as well as incorporating big data analysis while trying to better understand cultural production in the contemporary art world. In her PhD research, she is focusing on better understanding the ways underlying network structure, positions, and core-periphery power-relations influence the ways of incorporation of CEE artists into core art collections and global art canon. She is interested in topics related to post-WWII and contemporary art practices as well as in questions related to the sociology of art and culture. Before starting her PhD she worked as a member of the research group of Kassák Museum and published reviews and art theoretical essays in several art journals.