Smart cities surveil through ubiquitous and intrusive data collection via networked sensors. Smart city efforts are also frequently imagined as primarily top-down and male visions of the future in service of economic benefit. The smart campus presents a new dimension of smart city urbanism as an identified gap in literature. In the following dissertation, I trace sociotechnical imaginaries of a large R1 research university as a smart campus, using an Internet of Things (IoT) pole deployment as a case study. My primary research questions consider the localized co-production and imagining of the smart campus and its implications, explored through four research approaches: (1) interviews with designers, (2) archival review (3) qualitative analysis of visual case study artifacts, and (4) student interviews supported with observational study. Key findings include parallels to existing research on smart city imaginaries based in technosolutionistic, male-dominated, science fiction visions of the future. There is a reproduction of Big Data narratives of efficiency, and top-down implementation of visions extended to the smart campus. I also identify tensions in narratives of purpose, between privacy and surveillance, and digital citizenship dynamics. I employ an intersectional feminist technoscience lens rooted in Science and Technology Studies (STS), as well as notions of data justice and data power, from Critical Data Studies (CDS), and narrative inquiry methods to examine the stabilization and legitimization of these smart campus narratives. I also incorporate analysis of gender, power, and racialized surveillance relevant to carceral imaginaries in the smart campus. With support from narrative inquiry methods, I explore rhetoric underpinning the smart campus imaginary, specifically around promises of democracy, egalitarianism, and techno-utopias, tracing influences in national geopolitics, science fiction, and Silicon Valley ideology. This work contributes to existing knowledge on sociotechnical imaginaries of the smart city, documenting a genealogy as it is embedded into the urban space of the smart campus, and presenting an empirically grounded study of smart cities. This work also contributes to understandings of data as power, data assemblages, and data narratives, and to feminist technoscience literature on smart cities and the smart campus in the fields of CDS and STS.

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.

Mexico City has an ongoing air pollution issue that negatively affects its citizens and surroundings with current structural disconnections preventing the city from improving its overall air quality. Thematic methodological analysis reveals current obstacles and barriers, as well as variables contributing to this persistent problem. A historical background reveals current programs and policies implemented to improve Mexico’s City air quality. Mexico City’s current systems, infrastructure, and policies are inadequate and ineffective. There is a lack of appropriate regulation on other modes of transportation, and the current government system fails to identify how the class disparity in the city and lack of adequate education are contributing to this ongoing problem. Education and adequate public awareness can potentially aid the fight against air pollution in the Metropolitan City.

In the wake of the post-2000s internet and technology boom, with the nearly simultaneous introduction of smartphones, tablet, IPads, and online video streaming, another moral panic around pornography has reared its head. While much has been written about pornography from the perspective of media analysis and, more recently, ethnographic work of the industry and with performers themselves, very little work has been done with consumers. What has been undertaken, by psychologists and antiporn academics in particular, suffers an unfortunate lack of diversity in terms of how consumers are defined. That is, psychologists and antiporn academics alike appear to think only white hetero men consume porn. This research realizes its significance through the idea that porn looks and feels differently, and expresses different meanings through the historical and intersecting relations to power of a consumer, even in the young heterosexual men that antiporn feminists are so keen on using as a strawman for all porn consumption. With the help of an intersectional affects framework, I am able to articulate the manner in which pornography puts bodies in motion before the mind undertakes a hermeneutical exercise fundamentally framed by the consumer’s knowledge and subjectivity, which muddles how antiporn’s speech act approaches presume a direct propositional transmission from a pornographic object to the consumer. Moreover, a digital object of any kind becomes pornography when it is used as such (Magnus Ullén, 2013); there is no necessary or logical consequence that outside of such a context the object remains inherently or intentionally an object of pornography (Mary Mikkola, 2017). With the help of my participants, I expose the manner in which subjective and intersubjective flows of affects expose entanglements of hope, possibility, and cruelty for porn consumers qua affective subjects. This is particularly the case for those non-majoritarian subjects whose promise of sexual citizenship and/or legibility, within neoliberalism’s single-issue progress narrative and linear temporality, rests on both the transposition of illegibility and non-citizenship elsewhere, as well as the subject’s willingness to fix, label, and thereby commodify their desires as affective labor.