Abstract
This paper examines how multidimensional scaling, PROFIT analysis, co-occurrence data analysis, hierarchical clustering, and individual scaling can be used to map out symbolic systems of classification and measure some of their dimensions. Using data on prestige and boundaries among popular music artists in Québec, the paper shows how these techniques can be used to measure the nature, location, and salience of symbolic boundaries between groups. Multidimensional scaling and clustering provide an innovative and systematic approach for mapping out cognitive systems of classification, especially when boundaries are ill-defined, contested, or shifting.
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