UNRELIABLE CURATORS

EMMA & HANNAH

Humans are classifiers and our human made categories are always subject to change. While classifying we are actively producing knowledge. Every act of curating is interpretive. We are always situated culturally, historically, and politically and shaped by a specific worldview. Recognizing that curating data is subjective does not make the data pointless, it however makes for a more honest and ethical interpretation.

A critical approach to data is not dismissive but deepens our understanding of contemporary science. As anthropologists and historians in this course, we came with an approach to knowledge as being actively made by culture. We have come to realize that sciences from seemingly foreign grounds aren’t that different from us after all. We've been taught "to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world." D’Ignazio, C. & Klein, L. (2020)

With this course in mind, it has given us the tools to dismantle the power that lies hidden in curating, categorizing and visualizing data. We have examined how the inevitable role of selection in analysis affects the interpretations that can be made. We have tried to engage critically with histories that are difficult to capture in static categories, histories that are not one-sided but complex and intertwined with different ideas, places and people. The data we produce creates the epistemic grounds for how we interpret our world. When curating data, curators need to be aware that they are unreliable curators.

Drawing on the work from our three assignments this exhibition will highlight our unreliableness in the three stages of collecting, categorizing and visualizing data in curating data.

COLLECTING

Digitizing our bookshelves

Collecting involves a form of representation and displaying. Because we chose to work with books, something that is personally meaningful to us, we were aware that publicly displaying the books will give an impression of who we are. As we were aware of how we represent ourselves in an intellectual way, this unreliableness ultimately shaped our dataset in our favor.
We chose categories such as country of origin, genre and gender because they are meaningful and significant to us. These choices enabled us to make specific interpretations. Had the dataset been selected by a different curator or had we chosen different categories we could have highlighted different aspects of the books and different interpretations could have been made.
“As such, data are inherently partial, selective and representative, and the distinguishing criteria used in their capture have consequence” (Kitchin 2022, 5)

Caspar Voght Jr.

CATEGORIZING

When working with Caspar Voght Jr. we quickly realised that we were curating from within an unreliable historical tradition. Voght Jr. has been known as a merchant and generous reformer, but the histories not highlighted as his involvement with slave labor and trade, will continue to enforce colonial thought by concealing the exploitation his legacy was built on. In Wikidata no categories for his field of work could sufficiently dismantle the power in his legacy. Thus our own curation remained unreliable: we were challenging the story, but still working inside an ontology built from the very histories we sought to question.

“We need to recognize that all information systems are necessarily suffused with ethical and political values, modulated by local administrative procedures. These systems are active creators of categories in the world as well as simulators of existing categories”

Epistemic gap

VISUALIZING

Specific ways of presenting data lead to specific conclusions. When highlighting the average absence from work in relation to gender and the average indicator of well-being in the Folkeskole, the reasons behind the absence remain invisible, and the lived experiences behind well-being data disappear entirely. This makes us unreliable visualisers: even when aiming to be “objective,” as well as when we attempt to be “critical,” we inevitably promote particular narratives through our aesthetic and structural choices. As Donna Haraway argues in Situated Knowledges, “visual representations often hide the people, methods, questions, and messiness behind the clean lines and geometric shapes” (1988).

Bowker & Star in Sorting Things Out, 2000, p. 321

Haraway, Donna in Situated Knowleges (1988)

Kitchin, Rob. 2022. “Introducing Data.” In The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences, 1–19. SAGE Publications Ltd.