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With an abundance of sunshine, the state of Arizona has the potential for producing large amounts of solar energy. However, in recent years Arizona has also become the focal point in a political battle to determine the value and future of residential solar energy fees, which has critical implications for distributed generation. As the debate grows, it is clear that solar policies developed in Arizona will influence other state regulators regarding their solar rate structures and Net Energy Metering; however, there is a hindrance in the progress of this discussion due to the varying frameworks of the stakeholders involved. For this project, I set out to understand and analyze why the different stakeholders have such conflicting viewpoints. Some groups interpret energy as a financial and technological object while others view it is an inherently social and political issue. I conducted research in three manners: 1) I attended public meetings, 2) hosted interviews, and 3) analyzed reports and studies on the value of solar. By using the SRP 2015 Rate Case as my central study, I will discuss how these opposing viewpoints do or do not incorporate various forms of justice such as distributive, participatory, and recognition justice. In regards to the SRP Rate Case, I will look at both the utility- consumer relationship and the public meeting processes in which they interact, in addition to the pricing plans. This work reveals that antiquated utility structures and a lack of participation and recognition justice are hindering the creation of policy changes that satisfy both the needs of the utilities and the community at large.
ContributorsGidney, Jacob Robert (Author) / Richter, Jennifer (Thesis director) / Jurik, Nancy (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Software systems can exacerbate and cause contemporary social inequities. As such, scholars and activists have scrutinized sociotechnical systems like those used in facial recognition technology or predictive policing using the frameworks of algorithmic bias and dataset bias. However, these conversations are incomplete without study of data models: the structural, epistemological, and technical frameworks that shape data. In Modeling Power: Data Models and the Production of Social Inequality, I elucidate the connections between relational data modeling techniques and manifestations of systems of power in the United States, specifically white supremacy and cisgender normativity. This project has three distinct parts. First, I historicize early publications by E. F. Codd, Peter Chen, Miles Smith & Diane Smith, and J. R. Abrial to demonstrate that now-taken-for-granted data modeling techniques were products of their social and technical moments and, as such, reinforced dominant systems of power. I further connect database reification techniques to contemporary racial analyses of reification via the work of Cheryl Harris. Second, I reverse engineer Android applications (with Jadx and apktool) to uncover the relational data models within. I analyze DAO annotations, create entity-relationship diagrams, and then examine those resultant models, again linking them back to systems of race and gender power. I craft a method for performing a reverse engineering investigation within a specific sociotechnical context -- a situated analysis of the contextual epistemological frames embedded within relational paradigms. Finally, I develop a relational data model that integrates insights from the project’s reverse and historical engineering phases. In my speculative engineering process, I suggest that the temporality of modern digital computing is incommensurate with the temporality of modern transgender lives. Following this, I speculate and build a trans-inclusive data model that demonstrates uses of reification to actively subvert systems of racialized and gendered power. By promoting aspects of social identity to first-order objects within a data model, I show that additional “intellectual manageability” is possible through reification. Through each part, I argue that contemporary approaches to the social impacts of software systems incomplete without data models. Data models structure algorithmic opportunities. As algorithms continue to reinforce systems of inequality, data models provide opportunities for intervention and subversion.
ContributorsStevens, Nikki Lane (Author) / Wernimont, Jacqueline D (Thesis advisor) / Michael, Katina (Thesis advisor) / Richter, Jennifer (Committee member) / Duarte, Marisa E. (Committee member) / Arizona State University (Publisher)
Created2022