Querying the Quantification of the Queer: Data-Driven Visualisations of the Gender Spectrum

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Abstract

Critical data studies denounce that heteronormative formalisations of sex/gender identity obscure queer lives from datasets and computational processes. Since sex/gender categories cannot convey nonbinarity and multiplicity, this project addresses the inherent tension between queerness and systematisation with an anticategorical quantitative visual approach, combining psychometric scales with visualisations of the gender spectrum. Indeed, using queer and feminist methodologies, we propose a new data structure resulting from the codesign of an interactive visualisation of the gender spectrum, made in an iterative dialogue with gender-diverse people. Our contribution is twofold: an inclusive data structure for the self-assessment of sex/gender identity, and an interactive visualisation, acting as a critical design object, educational and counselling tool, and basis for data collection, annotation, and analysis. Although this project could both empower and harm sexual minorities, its originality is seeing nonbinarity and multiplicity not as appendices to a normative system, but as queering design principles.

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APA

Simeoni, F., Menéndez-Blanco, M., Vyas, R., & De Angeli, A. (2024). Querying the Quantification of the Queer: Data-Driven Visualisations of the Gender Spectrum. In Proceedings of the 2024 ACM Designing Interactive Systems Conference, DIS 2024 (pp. 3243–3256). Association for Computing Machinery, Inc. https://doi.org/10.1145/3643834.3660695

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