Abstract
This paper presents Sounding Territories, a sonic performance developed with Fundación Organizmo (Colombia) that explores dis/identificatory codings as a tactic for resisting and subverting algorithmic capture. Building on queer and more-than-human perspectives, the project reorients computational logics from stable classification toward relational and generative forms of identification. Through deep listening, embodied performance, and model training, datasets were composed as relational constellations: entanglements of sounds and situations (e.g., fire–conversation–bird–wind) that enact the liveliness of data beyond representational frames. Rather than seeking legibility within existing taxonomies, dis/identification becomes a methodological reorientation–a performance of politics–that disturbs predictive logics and embraces illegibility as generative of plural and interdependent life/worlds. The paper contributes to more-than-human data practices by advancing dis/identificatory coding as a mode of relational worlding.
Keywords
Algorithmic identification, dis/identification, generative sonic modeling, performative politics
DOI
https://doi.org/10.21606/drs.2026.2162
Citation
Turtle, G., Kotowski, B., Giaccardi, E., and Bendor, R. (2026) Sounding Territories: Dis/identificatory Codings as Relational Worlding, 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.2026.2162
Creative Commons License
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Included in
Sounding Territories: Dis/identificatory Codings as Relational Worlding
This paper presents Sounding Territories, a sonic performance developed with Fundación Organizmo (Colombia) that explores dis/identificatory codings as a tactic for resisting and subverting algorithmic capture. Building on queer and more-than-human perspectives, the project reorients computational logics from stable classification toward relational and generative forms of identification. Through deep listening, embodied performance, and model training, datasets were composed as relational constellations: entanglements of sounds and situations (e.g., fire–conversation–bird–wind) that enact the liveliness of data beyond representational frames. Rather than seeking legibility within existing taxonomies, dis/identification becomes a methodological reorientation–a performance of politics–that disturbs predictive logics and embraces illegibility as generative of plural and interdependent life/worlds. The paper contributes to more-than-human data practices by advancing dis/identificatory coding as a mode of relational worlding.