Abstract
Queer(ing) is a practice of questioning, unsettling, and liberating that resists normative ways of knowing. This paper advances a Queer(ing) epistemology for intersectional data science and offers TMI (Too Much Information)-WEB as a working case. This open source, theory-driven qualitative data analysis ecosystem treats identity, lived experience, and power as relational, contextual, and co-constructed. Built on a human-centered graph data model, it reimagines databases as dynamic knowledge systems rather than neutral containers. Authentic, identity-driven personas emerge from participants’ own narratives, while affective queries invite researchers to explore (and experience) patterns of harm, coping, joy, and self-acceptance across intersectional lives and social scenarios. We argue that Queer(ing) epistemology shifts data science from describing populations to knowing with people—centering embodiment, vulnerability, and relational dynamics. In doing so, TMI-WEB demonstrates how Queer(ing) epistemology through intersectional data science can generate open, complex, hopeful ways of knowing—otherwise.
Keywords
Queering Epistemology, Intersectional Data Science, Affective Queries, TMI-WEB
DOI
https://doi.org/10.21606/drs.2026.483
Citation
Westbrook, J.P., and Ehmke, C.A. (2026) Queer(ing) Epistemology by Design: TMI-WEB—A Relational Knowledge System for Intersectional Data Science and Affective Queries, in Simeone, L., Gray, C. M., Verhoeven, A., de Götzen, A., Bakırlıoğlu, Y., Zohar, H., Stead, M., and Buwert, P. (eds.), DRS2026: Edinburgh, 8–12 June, Edinburgh, United Kingdom. https://doi.org/10.21606/drs.2026.483
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Included in
Queer(ing) Epistemology by Design: TMI-WEB—A Relational Knowledge System for Intersectional Data Science and Affective Queries
Queer(ing) is a practice of questioning, unsettling, and liberating that resists normative ways of knowing. This paper advances a Queer(ing) epistemology for intersectional data science and offers TMI (Too Much Information)-WEB as a working case. This open source, theory-driven qualitative data analysis ecosystem treats identity, lived experience, and power as relational, contextual, and co-constructed. Built on a human-centered graph data model, it reimagines databases as dynamic knowledge systems rather than neutral containers. Authentic, identity-driven personas emerge from participants’ own narratives, while affective queries invite researchers to explore (and experience) patterns of harm, coping, joy, and self-acceptance across intersectional lives and social scenarios. We argue that Queer(ing) epistemology shifts data science from describing populations to knowing with people—centering embodiment, vulnerability, and relational dynamics. In doing so, TMI-WEB demonstrates how Queer(ing) epistemology through intersectional data science can generate open, complex, hopeful ways of knowing—otherwise.