Alexander Tripp
Biographic Data
| ID | 4123786 |
|---|---|
| NAME | Alexander Tripp |
| GIVEN NAMES | Alexander |
| FAMILY NAME | Tripp |
| SIGNATURE | TRIPP A |
| AFFILIATIONS | Vanderbilt University |
| ORCID | 0009-0007-2574-7038 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Pipeline Diversity via Career Diversity
Fostering diversity in political science careers is important. Undergraduate research experiences, coupled with an emphasis on career diversity, have the potential to increase relevant knowledge about and buoy tendencies toward pursuing a PhD among students from diverse backgrounds. This article describes components of a US National Science Foundation–funded Research Experiences for Undergraduates (REU) program that highlighted career diversity. …
Benchmarking AI and human text classifications in the context of newspaper frames
I examine the abilities of large language models (LLMs) to accurately classify topics related to immigration from Spanish-language newspaper articles. I benchmark various LLMs (ChatGPT and Claude) and undergraduate coders with my own codings. I prompt models to label articles with either an 8 label scheme—directly analogous to the assignment of the undergraduate coders—or a 4 label scheme—aggregating the 8 labels into broader themes. In my analys…
No prominent works on this page.
Pipeline Diversity via Career Diversity
Fostering diversity in political science careers is important. Undergraduate research experiences, coupled with an emphasis on career diversity, have the potential to increase relevant knowledge about and buoy tendencies toward pursuing a PhD among students from diverse backgrounds. This article describes components of a US National Science Foundation–funded Research Experiences for Undergraduates (REU) program that highlighted career diversity. …
Benchmarking AI and human text classifications in the context of newspaper frames
I examine the abilities of large language models (LLMs) to accurately classify topics related to immigration from Spanish-language newspaper articles. I benchmark various LLMs (ChatGPT and Claude) and undergraduate coders with my own codings. I prompt models to label articles with either an 8 label scheme—directly analogous to the assignment of the undergraduate coders—or a 4 label scheme—aggregating the 8 labels into broader themes. In my analys…
Artificial Intelligence (1 works) · Benchmarking (1 works) · Career Development and Diversity (1 works) · Computer Science (1 works) · Context (archaeology (1 works) · Diversity (politics (1 works) · Economics (1 works) · Engineering (1 works) · Engineering ethics (1 works) · Higher Education and Employability (1 works)