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Community-collaborative approaches (CCA) have been proposed as more equitable ways to engage communities in research, as they urge researchers to commit to long-term relationships with community members than with other participatory methods. However, the normative structures of HCI and computing research can present challenges in pursuing CCA for the researchers and community partners involved. This paper offers insights into: i) how research and relation impact each other, and ii) how we can conceptualize research as a mode of relation. We present our findings from eighteen semi-structured interviews with community-collaborative researchers in computing and HCI. We then ground our paper in theories of relation and relationality from Caribbean thought, Black studies, and Indigenous scholarship to apply a conceptual framework of relation to our findings. Through this work, we aim to interrogate what it means to center relationality in CCA, beyond and within the development of scientific research. 

Climate change demands urgent action, yet understanding the environmental impact (EI) of everyday objects and activities remains challenging for the general public. While Life Cycle Assessment (LCA) offers a comprehensive framework for EI analysis, its traditional implementation requires extensive domain expertise, structured input data, and significant time investment, creating barriers for non-experts seeking real-time sustainability insights. Here we present the first autonomous sustainability assessment tool that bridges this gap by transforming unstructured natural language descriptions into in-context, interactive EI visualizations. Our approach combines language modeling and AI agents, and achieves >97% accuracy in transforming natural language into a data abstraction designed for simplified LCA modeling. The system employs a non-parametric datastore to integrate proprietary LCA databases while maintaining data source attribution and allowing personalized source management. We demonstrate through case studies that our system achieves results within 11% of traditional full LCA, while accelerating from hours of expert time to real-time. We conducted a formative elicitation study (N=6) to inform the design objectives of such EI communication augmentation tools. We implemented and deployed the tool as a Chromium browser extension and further evaluated it through a user study (N=12). This work represents a significant step toward democratizing access to environmental impact information for the general public with zero LCA expertise. 

In this paper, we seek to understand how grassroots activists, operating within the hegemony of data-centrism, are often disempowered by data even as they appropriate it towards their own ends. We posit that the shift towards data-driven governance and organizing, by elevating a particular epistemology, can pave over other ways of knowing that are central to social movement practices. Building on Muravyov's [102] concept of ''epistemological ambiguity,'' we demonstrate how data-focused activism requires complex navigations between data-based epistemologies and the heterogeneous, experiential, and relational epistemologies that characterize social movements. Through three case studies (two drawn from existing literature and the third being an original analysis), we provide an analytical model of how generative epistemological refusals can support more value-aligned navigations of epistemological ambiguity that resist data-centrism. Finally, we suggest how these findings can inform pedagogy, research, and technology design to support communities navigating datafied political arenas.