Short Abstract

Through a collective photo-ethnography of everyday fixings, this workshop explores data foraging as a resistant, situated practice—reimagining datasets as partial acts of embodied attention against the extractive smoothness of data mining.

Description

What is done to keep togetherness? Why is everything not falling apart? Fixings are those improvised gestures that not-only-humans perform to stabilize the world in temporary ways—a folded paper under a wobbly table, tape sealing a crack, a rubber band closing a bag. At a time when AI tends to represent the world as seamless, coherent, and optimizable we are surrounded by material reminders that resist such idealizations. Following Annemarie Mol’s notion of multiplicity and Mike Michael’s notion of the mundane each daily fixing becomes a theory-practice of the world, a specific enactment of the thick present that perhaps says more about us than those unfixed, unbroken versions of it—if such versions even exist.

This workshop emerges from the friction between (supposed) computational perfection and the uneven textures of lived worlds. It asks: what kinds of images, what forms of data, do we want to stand for? Through the practice of data foraging, participants will explore an alternative way of engaging with data, one grounded in situated attention, embodied observation, and collective making. Instead of gathering data to expose patterns aimed at prediction and efficiency, the practice of foraging invites participants to slow down, observe their closest surroundings, and document the subtle arrangements that keep things (and us) held together.

The session begins with a short conceptual introduction. Participants then embark on a photo-ethnographic walk around the venue and its surroundings. Then the group will build a small dataset, while sustaining a discussion on what these images reveal about the everyday infrastructures that support living together. The outcome will be a public small database and archive of situated data practices.

This workshop invites the STS community to engage data critically: fixing not what is broken, but what is possible.