Author ORCID Identifier

https://orcid.org/0000-0001-7189-269X

Date Available

7-19-2022

Year of Publication

2020

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Arts and Sciences

Department/School/Program

Anthropology

First Advisor

Dr. Ann Kingsolver

Abstract

In Chattanooga, TN, the construction of a fiber optic telecommunications network has led to a tech-based revitalization strategy, and the promotion of entrepreneurial and technical positions within the Downtown. This dissertation questions what revitalization, the “Chattanooga Way,” means to differently situated residents of Chattanooga, TN, and how those differences in interpretation are related to lived experiences of economic inequality. Powerful local discourses, like the Chattanooga Way policy model and its accompanying “origin myth” of Chattanooga’s development often conceal disparities between grass-roots, public, and private sector notions of economic revitalization. Through the projection of a tech-based economic future, Chattanooga has created a singular history of its socio-economic progress, which rests on the erasures not only of alternative historical discourses, but also on an unmarked “social field of whiteness” (Hargrove 2009) perpetuated by the concentration of development capital Downtown. In focusing on the types of work valorized in public discourse, this dissertation argues for the future of work as contingent, and the need for a policy agenda which takes varied labor forms and livelihood strategies into account. The experience of contingency is examined across classes, rather than in terms of a class in the making (such as the precariat), to demonstrate how revitalization discourses and historical discourses perpetuate social disparities despite the connectedness of workers via workforce restructuring. In doing so, this dissertation highlights the connections between the individual experience of work, the construction of livelihoods, and the social interpretation of work as related to large-scale processes.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2020.288

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