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Outstanding Dissertation Awards

The Graduate School's Outstanding Dissertation Award recognizes exceptional scholarship, research, and writing by doctoral students. These awards are intended to help raise the profile of our doctoral students and identify dissertations worthy of nomination for various national awards, including the prestigious Council of Graduate Schools / UMI Distinguished Dissertation Award.

Awards are given each academic year for Humanities, Sciences, and Social Sciences. The winners will receive a $1000 cash prize and will be honored at the Outstanding Graduate Student Recognition Luncheon.

Criteria

To be eligible for the 2016 award, the dissertation must be defended and submitted to the Graduate School in time for commencement in August 2015, December 2015, or May 2016.

Nominations

Near the spring deadline for defense date, the Graduate School will issue a call for nominations. Graduate Program Directors are asked to submit one nominee per department.

After receiving all of the nominations, the Graduate School will notify each of the selected students that their project has been nominated and is thereby recognized as the outstanding dissertation from their department.

Nomination Materials
  • A letter of nomination (no more than one page) from the Graduate Program Director
  • A letter of support from the dissertation director or a committee member, explaining the project's research and significance in a manner similar to that used for letters of recommendation for faculty jobs. Letters should address the criteria listed below and be no more than three pages long. A sample recommendation letter may be found here .
  • An abstract of the dissertation (no more than five pages) by the author.
Evaluation

A selection committee comprised of Graduate Program Directors will determine the winners from among the department nominees. The dissertations will be evaluated according to the following criteria:

  • Significance
  • Originality
  • Soundness of Methodology/Argumentation
  • Research/Data