Date of Award

1-1-2013

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Sociology

Content Description

1 online resource (iv, 176 pages) : illustrations (some color)

Dissertation/Thesis Chair

Ronald N Jacobs

Committee Members

Glenn Deane, Richard Lachmann

Keywords

cultural taste, multilevel modeling, neighborhood effects, peer effects, Culture, Group identity, Cultural fusion, Social mobility

Subject Categories

Sociology

Abstract

After a review of the "cultural omnivore" debate in sociology, the following thesis proposes an examination of how place "matters" for cultural taste. The Anglophone response to Bourdieu's (1984) Distinction that the U.S. elite forgo highbrow snobbism for wide-ranging "omnivore" cultural taste contains several assumptions about culture and its relationship to socioeconomic mobility. These responses, collectively known here as the "omnivore debate" are critiqued on the basis that they pay inadequate attention to the contexts surrounding cultural taste. But it is knowledge of context that is required to understand how taste might be related to socioeconomic mobility. In fact, none of Bourdieu's critics in the debate offers a systematic examination of the social environments of Bourdieu's France or their own U.S. research settings. This critique serves as a springboard to break from an approach that is dependent upon social stratification and social mobility frameworks. Instead, this thesis explores whether it is more helpful at times within omnivore research to view cultural taste using "symbolic boundaries" or "culture as repertoire" perspectives. Insights from the social interactions literature in econometrics are then used to develop a multilevel model of social space which synthesizes both individual and community factors and symbolic and structural factors into one comprehensive framework. This analytical model is then used in an empirical analysis of U.S. neighborhood effects on cultural taste. Hierarchical Bayesian modeling is combined with other approaches in an attempt to overcome several methodological challenges including those related to identification, selection bias, and sparse data.

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