The instructions and procedures for the 2018-2019 CGU Dissertation Fellowship awards may be downloaded here for reference. Applications are due on April 6, 2018.
Students with suitably transdisciplinary thesis projects may also apply for the transdisciplinary fellowships, for which deadlines and other application details will be posted on the transdisciplinary web site.
Eligibility requirements to apply for these awards include:
Please address any questions about the application process to the Faculty Research Committee via the Office of Research and Sponsored Programs.
For information on where to explore outside sources of support for dissertation research, the Office of Advancement offers a personal fellowships search service — please contact the advisor at firstname.lastname@example.org. You may also consult the Financial Aid site, portals for individual schools, and the Transdisciplinary Studies program. There are also useful online search engines for graduate student funding sources on the UCLA and Cornell websites.
Divvying Up Dollars: Experimental Applications of a Survey-Based, Budgeting Game to the Assessment of Stated Preferences for Public Spending
While public opinion polling has examined policy issues surrounding federal spending and budget deficits, it has not fully captured the complexity of voters' preferences regarding public spending. Building on the public choice literature, this dissertation employs a budget allocation game (AGAME) adapted from Beardsley, Kovenock, and Reynolds (1974) to understand voters' budgetary preferences. The adapted instrument simulates realistic tradeoffs faced in national budgeting and allows for measurement of voter preferences for tax increases or reductions, debt repayment, and eleven categories of government program spending including unemployment benefits, defense, education, housing, and science. A series of experimental applications will study the value of this improved methodological approach while examining the effects of policy relevant information on stated preferences for public spending.
Preventing College Student Prescription Stimulant Misuse: An Application of Vested Interest Theory
Vested Interest Theory suggests that the perceived importance and hedonic relevance of an expected behavioral outcome affects attitude-behavior consistency. Applied to college students' nonmedical use of prescription stimulants (NUPS), the theory suggests that attitudes alone will not predict usage, because the attitude-behavior relation is moderated by vested interest. To limit NUPS, persuasive information must affect not only attitudes, but also vested perceptions regarding stimulant use and college success. This research is designed to influence attitudes toward NUPS and perceptions of NUPS' role in college success. These cognitions are hypothesized to affect college students' resistance to, or cessation of NUPS.
The Dark Side of President Woodrow Wilson's Progressivism: Its Racism/Ethnocentrism
The dark side of Woodrow Wilson's Progressivism, that is, its racism/ethnocentrism, is brought into an original light. Wilson's political thought is shown to be a historicism informed by his underlying racist world view. Wilson departs from Lincoln's Second Founding and the 1787 Founding insofar as Wilson repudiated the equality principle of the Declaration of Independence on historicist grounds. Wilson's racist historicism is shown to contain elements from Hegel and Social Darwinism, and his idea of Providence. Wilson's thought is shown to be an example of the American white supremacist tradition justifying his strengthening of the Jim Crow regime.
Stochastic Iterative Algorithms for Large-Scale Data Analysis
Advances in technology have led to a world where large-scale data collection is ubiquitous. However, traditional techniques for processing data are not designed for such large-scale data sets, and are thus quickly becoming outdated. As a result, there is an immense demand for efﬁcient, scalable, and robust algorithms for data analytics. Interest in a speciﬁc class of algorithms, Stochastic Iterative Algorithms, has grown in recent years due to their ability to handle large-scale data. This work aims to adapt, improve, and design algorithms for large-scale data analytics, as well as provide theoretical guarantees for algorithmic performance.
Allyship at Work: Going beyond Diversity Policies and Practices
How can historically privileged (e.g., White) employees be allies to historically marginalized (e.g., Black) employees? This mixed-method dissertation will document privileged and marginalized employee perspectives on exemplary (i.e., extraordinarily committed) allyship. Study 1 will qualitatively interview 15 exemplars to catalogue their virtues and relational behaviors. Study 2 will quantitatively examine whether exemplars (n = 50) differ from comparison lay employees (n = 50) on hypothesized virtues and relational behaviors, and gather inclusion stories. Study 3 will experimentally test whether marginalized employees (N = 150) perceive allies' (versus lay employees') relational behaviors as more inclusive and, in turn, intend to behave prosocially.