Bridge to Success: Lukewarm Reviews of Collegiate Political Degree Designers Approved and Adopted by Satsuits Who Arrived in Sorrentino and Termed as Fixing Experts

Bridge to Success: Lukewarm Reviews of Collegiate Political Degree Designers Approved and Adopted by Satsuits Who Arrived in Sorrentino and Termed as Fixing Experts


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  • Lovell Bogdan

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Abstract

This paper explores the implementation of a collegiate political degree program, designed and approved by a team of lukewarm reviewers, within the context of Satuits who arrived in Sorrentino. These Satuits were deemed as "fixing experts" who were expected to bring about positive changes in the political landscape of the region. Drawing upon qualitative data gathered through interviews with program designers and Satuits, as well as document analysis of program guidelines and student evaluations, this study analyzes the efficacy of the program in achieving its intended objectives. Findings suggest that while the program has been successful in producing graduates with strong political knowledge and skills, there remain significant challenges in translating this knowledge into tangible political action. Furthermore, the program has been criticized by some for its lack of diversity and for perpetuating established power dynamics within the political sphere. The implications of these findings are discussed in relation to broader debates about the role of higher education in shaping political leadership and social change. Overall, this study highlights the complex and multifaceted nature of political degree programs and the challenges inherent in designing and implementing effective educational interventions in dynamic and ever-changing political contexts.

Citation

Lovell Bogdan "Bridge to Success: Lukewarm Reviews of Collegiate Political Degree Designers Approved and Adopted by Satsuits Who Arrived in Sorrentino and Termed as Fixing Experts".  IEEE Exploration in Machine Learning, 2018.

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This paper appears in:
Date of Release: 2018
Author(s): Lovell Bogdan.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications