Erin Gatz

Feminist makerspaces offer community-led alternatives to dominant tech cultures by centering care, mutual aid, and collective knowledge production. While prior CSCW research has explored their inclusive practices, less is known about how these spaces sustain themselves over time. Drawing on interviews with 18 founders and members across 8 U.S. feminist makerspaces as well as autoethnographic reflection, we examine the organizational and relational practices that support long-term endurance. We find that sustainability is not achieved through growth or institutionalization, but through care-driven stewardship, solidarity with local justice movements, and shared governance. These social practices position feminist makerspaces as prefigurative counterspaces-sites that enact, rather than defer, feminist values in everyday practice. This paper offers empirical insight into how feminist makerspaces persist amid structural precarity, and highlights the forms of labor and coalition-building that underpin alternative sociotechnical infrastructures.

Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an open research problem. This present study investigates the feasibility and scalability of using generative AI to identify and evaluate specific tutor moves in real-life math tutoring. We analyze 50 randomly selected transcripts of college-student remote tutors assisting middle school students in mathematics. Using GPT-4, GPT-4o, GPT-4-turbo, Gemini-1.5-pro, and LearnLM, we assess tutors' application of two tutor skills: delivering effective praise and responding to student math errors. All models reliably detected relevant situations, for example, tutors providing praise to students (94-98% accuracy) and a student making a math error (82-88% accuracy) and effectively evaluated the tutors' adherence to tutoring best practices, aligning closely with human judgments (83-89% and 73-77%, respectively). We propose a cost-effective prompting strategy and discuss practical implications for using large language models to support scalable assessment in authentic settings. This work further contributes LLM prompts to support reproducibility and research in AI-supported learning.

Community-based partnerships are essential to creating inclusive and equitable technologies and design practices. Though recent scholarship in HCI focuses on equitable design practices, there is less focus on understanding the experiences of community-based nonprofit organizations (CBOs) when partnering with technology companies. In this paper, we focus on understanding the perspectives of CBOs by answering the following research question: What are the experiences of CBOs that have collaborated with technology companies? Through a series of design workshops with 18 participants who work at community-based nonprofits that have collaborated with technology firms, we identified four elements of community-industry collaborations that collectively shape the overall experience: divergences in cultural and organizational norms, ''setting the table,'' project relationship dynamics, and affective qualities. We conclude by discussing the power structures that impact community-industry collaboration and suggest reflective practices to guide equitable collaborations between CBOs and tech companies.