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In the afternoon (Friday September 13, 12:30-3:30pm), Dr. Kristen Goessling and her colleague Carly Pourzand (Director of Community-Driven Impact, Participatory Research, Penn State Center Philadelphia) will organize a workshop on Participatory Action Research and practice with immigrant communities. See HERE for more information on their work, featured in last week’s Penn State News! Goessling is an expert on participatory action research (PAR), and uses this approach to investigate personal experiences of public policies with youth, students, and community members as co-researchers. Pourzand is an expert on community-driven engagement and impact, and uses this approach to shape and implement community-driven education and research initiatives that foster social changes.
“Critical Participatory Action Research: A Relational Approach to Transformative Research & Practice”
In this talk, Kristen Goessling weaves together stories from her engaged scholarship, research, and community organizing that reflect her approach to participatory action research as a practice of living inquiry. She presents a research praxis framework that explicitly centers issues of relationality, power, and participation. Through the sharing stories of struggles and emergent questions from her participatory research and practice, she invites participants into a
conversation to explore the values and vision that underpin transformative critical scholarship.
Dr. Di Liu will present an NRT LinDiv organized colloquium on Friday, February 16, 2024 at 9:00AM in the Foster Auditorium on PSU University Park campus and via Zoom.
Talk title TBA.
More information about Dr. Liu is available at the webpage below:
https://education.temple.edu/about/faculty-staff/di-liu-tum92163
Friday, February 10, 2023, 9:00–10:30 a.m. EST, 127 Moore Building and virtually via Zoom
Dr. Shomir Wilson
Assistant Professor and Director of the Human
Language Technologies Lab in the
College of Information Sciences and Technology
at Penn State
“Sociodemographic Biases in Natural Language Processing: Two Case Studies”
Large language models (LLMs) are widely used in natural language processing (NLP) to
obtain high performance on a variety of tasks. However, the large corpora used to train
these models contain sociodemographic biases, and LLMs tend to inherit those biases, with
potentially harmful results. Shomir Wilson will present two case studies that reveal the
sociodemographic biases of select LLMs within the context of sentiment analysis, a
common NLP task. The first study shows that Word2Vec and GloVe exhibit negative
sentiment bias toward terms for people with disabilities. The second study shows that GPT-
2 exhibits a range of sentiment biases for nationality demonyms, i.e., words that specify
national origins. Shomir will conclude with some thoughts on the significance of these
biases and the challenges to mitigating or eliminating them.