INVISIBLE LABOR FOR DATA: INSTITUTIONS, INFRASTRUCTURE, AND VIRTUAL SPACE

Americans are accustomed to a wide range of data collection in their lives: census, polls, surveys, user registrations, and disclosure forms. When logging onto the Internet, users’ actions are being tracked everywhere: clicking, typing, tapping, swiping, searching, and placing orders. All of this data is stored to create data-driven profiles of each user. Social network sites, furthermore, set the voluntarily sharing of personal data as the default mode of engagement. But people’s time and energy devoted to creating this massive amount of data, on paper and online, are taken for granted. Few people would consider their time and energy spent on data production as labor. Even if some people do acknowledge their labor for data, they believe it is accessory to the activities at hand. In the face of pervasive data collection and the rising time spent on screens, why do people keep ignoring their labor for data? How has labor for data been become invisible, as something that is disregarded by many users? What does invisible labor for data imply for everyday cultural practices in the United States? Invisible Labor for Data addresses these questions. I argue that three intertwined forces contribute to framing data production as being void of labor: data production institutions throughout history, the Internet’s technological infrastructure (especially with the implementation of algorithms), and the multiplication of virtual spaces. There is a common tendency in the framework of human interactions with computers to deprive data and bodies of their materiality. My Introduction and Chapter 1 offer theoretical interventions by reinstating embodied materiality and redefining labor for data as an ongoing process. The middle Chapters present case studies explaining how labor for data is pushed to the margin of the narratives about data production. I focus on a nationwide debate in the 1960s on whether the U.S. should build a databank, contemporary Big Data practices in the data broker and the Internet industries, and the group of people who are hired to produce data for other people’s avatars in the virtual games. I conclude with a discussion on how the new development of crowdsourcing projects may usher in the new chapter in exploiting invisible and discounted labor for data.

THE DEVELOPMENT EFFECTIVENESS OF INTERNATIONAL WATER AND SANITATION INFRASTRUCTURE PROJECTS: DEFINING “QUALITY AT ENTRY” OF WORLD BANK PROJECTS.

Over the past 15 years, the number of international development projects aimed at combating global poverty has increased significantly. Within the water and sanitation sector however, and despite heightened global attention and an increase in the number of infrastructure projects, over 800 million people remain without access to appropriate water and sanitation facilities. The majority of donor aid in the water supply and sanitation sector of developing countries is delivered through standalone projects. The quality of projects at the design and preparation stage is a critical determinant in meeting project objectives. The quality of projects at early stage of design, widely referred to as quality at entry (QAE), however remains unquantified and largely subjective. This research argues that water and sanitation infrastructure projects in the developing world tend to be designed in the absence of a specific set of actions that ensure high QAE, and consequently have relatively high rates of failure. This research analyzes 32 cases of water and sanitation infrastructure projects implemented with partial or full World Bank financing globally from 2000 – 2010. The research uses categorical data analysis, regression analysis and descriptive analysis to examine perceived linkages between project QAE and project development outcomes and determines which upstream project design factors are likely to impact the QAE of international development projects in water supply and sanitation. The research proposes a number of specific design stage actions that can be incorporated into the formal review process of water and sanitation projects financed by the World Bank or other international development partners.

Modeling Flood Stage-Duration-Frequency: A Risk Assessment of Critical Infrastructure in the Tidal Potomac

The service of a critical infrastructure, such as a municipal wastewater treatment plant (MWWTP), is taken for granted until a flood or another low frequency, high consequence crisis brings its fragility to attention. The unique aspects of the MWWTP call for a method to quantify the flood stage-duration-frequency relationship. By developing a bivariate joint distribution model of flood stage and duration, this study adds a second dimension, time, into flood risk studies. A new parameter, inter-event time, is developed to further illustrate the effect of event separation on the frequency assessment. The method is tested on riverine, estuary and tidal sites in the Mid-Atlantic region. Equipment damage functions are characterized by linear and step damage models. The Expected Annual Damage (EAD) of the underground equipment is further estimated by the parametric joint distribution model, which is a function of both flood stage and duration, demonstrating the application of the bivariate model in risk assessment. Flood likelihood may alter due to climate change. A sensitivity analysis method is developed to assess future flood risk by estimating flood frequency under conditions of higher sea level and stream flow response to increased precipitation intensity. Scenarios based on steady and unsteady flow analysis are generated for current climate, future climate within this century, and future climate beyond this century, consistent with the WWTP planning horizons. The spatial extent of flood risk is visualized by inundation mapping and GIS-Assisted Risk Register (GARR). This research will help the stakeholders of the critical infrastructure be aware of the flood risk, vulnerability, and the inherent uncertainty.

RESILIENCE OF NETWORKED INFRASTRUCTURE WITH EVOLVING COMPONENT CONDITIONS: A PAVEMENT NETWORK APPLICATION

This thesis deals with quantifying the resilience of a network of pavements. Calculations were carried out by modeling network performance under a set of possible damage-meteorological scenarios with known probability of occurrence. Resilience evaluation was performed a priori while accounting for optimal preparedness decisions and additional response actions that can be taken under each of the scenarios. Unlike the common assumption that the pre-event condition of all system components is uniform, fixed, and pristine, component condition evolution was incorporated herein. For this purpose, the health of the individual system components immediately prior to hazard event impact, under all considered scenarios, was associated with a serviceability rating. This rating was projected to reflect both natural deterioration and any intermittent improvements due to maintenance. The scheme was demonstrated for a hypothetical case study involving Laguardia Airport. Results show that resilience can be impacted by the condition of the infrastructure elements, their natural deterioration processes, and prevailing maintenance plans. The findings imply that, in general, upper bound values are reported in ordinary resilience work, and that including evolving component conditions is of value.