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Mobile Classroom: City Farm, SLO
Get PDFThe Mobile Classroom project is one element of the larger “Garden for All” project at City Farm SLO. The “Garden for All” project has created a garden that is accessible to mobility limited people, such as those in wheelchairs. The Mobile Classroom is 3 picnic tables with 6 movable benches. The tables are not be connected to the benches which allow a person in a wheelchair to roll up to the table. The picnic tables are 6’ long, 40” wide, and 29” tall. The benches are 6’ long, 14.5” wide, and 18” tall. The supporting elements of the tables and benches are Douglass Fir. The top elements are Redwood. The Tables and benches have matching design and material for aesthetic value. Additionally, the client requested a lettuce wash table. This table is 8’ tall, 3’ wide, and 3’ tall. The table has four legs of Douglas Fir. The wash table has a ½” hardware cloth top which will allow water permeability when washing lettuce. All wooden elements have been treated with clear coat decking sealer. The goal is to have the tables and benches last a long time and withstand the elements. The total cost of this project was $1,269
The Feminist Keyword Project: Literature Reviews as Feminist Praxis
Get PDFThe Feminist Keyword Project is a scaffolded series of assignments that culminates in a literature review of the student’s chosen keyword. It is designed for intermediate undergraduate students who acquired a foundational feminist vocabulary in their introductory classes. Building upon this baseline fluency, the project shifts students’ understanding of course concepts from a glossary of terms to politicized feminist gatherings. The project is informed by the tradition of the keyword entry genre to move students beyond merely defining their chosen term to narrating it as a site of feminist discourse in and outside of academia. In doing so, the Feminist Keyword Project engages students in the feminist praxis of research, writing, and citation
Ambiguous Identities: Gesturing Towards an Intersectional Conception of Freedom
Get PDFWriting in The Ethics of Ambiguity (1948), existential philosopher and feminist theorist Simone de Beauvoir declared that each individual’s freedom depends upon that of others. This claim was meant to motivate others to not remain complicit in the oppression of others; however, when considering the xenophobic rhetoric within Western feminists’ rhetoric about “liberating” Muslim women, one realizes that this demand warrants further scrutiny. In this paper, I apply Alia Al-Saji’s work on Western feminists’ approaches to liberating “other” women to de Beauvoir’s “we” in order to strengthen this latter concept. Overall, my aim with this work is to demonstrate that an intersectional understanding of “we” is necessary for collective resistance efforts to avoid perpetuating other forms of oppression.Keywords: “we” as legion, Western feminist rhetoric, initial hesitatio
Improving Semantic Document Classification Accuracy by Integrating Human-Crafted Knowledge
Get PDFDocument classification is a pivotal task in various domains, warranting the development of robust algorithms. Among these, the Bidirectional Encoder Representations from Transformers (BERT) algorithm, introduced by Google, has proven to perform well when fine-tuned for the task at hand. Leveraging transformer architecture, BERT demonstrates stellar language understanding capabilities. However, the integration of BERT with a range of techniques has shown potential for further enhancing classification accuracy. This work investigates several techniques that leverage semantic understanding to improve the performance of document classification models trained with BERT. Specifically, we explore three methods. First, we will balance corpuses afflicted by imbalanced training data distributions. Next, we substitute particular words with semantically similar words when balancing to create “synthetic documents,” thereby shifting the model\u27s focus from individual words to semantic meaning. Finally, we retrain the model on a dataset comprised of “synthetic documents” with heavier weights given to classes with commonly misclassified documents during the initial round of training. These approaches emphasize the significance of semantic comprehension in document classification, as the meaning of words and phrases often relies on contextual cues. Our findings demonstrate the efficacy of these techniques and highlight the importance of incorporating semantic knowledge in document classification algorithms. On our labeled testing set of news articles from BBC News, BERT performs with a baseline macro F1 score of 0.902. Using all of our techniques, we have improved the macro F1 score to 0.951