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. With this course we have come to realize that sciences from seemingly foreign grounds aren’t that different from us after all. A fusion of natural science and humanities opens a space of possibilities for different interpretations, perspectives and science practices.

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. With this course in mind, it has given us the tools to dismantle the power that lies hidden in curating, categorizing and visualizing. 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.

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

COLLECTING

Digitalizing our bookshelves

Throughout the process of digitizing our books, it became clear that this is not a neutral process. When digitizing the books we remove them from the practical, physical context and, in doing so, leave out many details, impressions, and nuances. The data included to describe and digitize the books is not an entirely accurate reflection of the books themselves but a partial reconstruction.
“(...)we must recognize that quantification is “both essential and
insufficient, dehumanizing and reparative, necessary and complicated.”

Caspar Voght Jr.

CATEGORIZING

Decolonizing data is not just about adding more stories but about questioning the very architecture of categorization itself. The past resists being flattened into discrete, computable facts and yet, these acts of translation are how knowledge becomes shareable.
“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”

The categories, the properties, the connections are all human inventions, held together by consensus, convenience, and power.

Epistemic gap

VISUALIZING

While the first visualizations might appear objective and neutral they also work rhetorically by a promoting a specific narrative that carries political implications.

“Any communicating object that reflects choices about the selection and representation of reality is a rhetorical object”

The clarity in the first visualizations also comes with epistemic costs because it strips the data of its social context.

“It’s also the most ethically complicated to navigate for the ways in which it masks the people, the methods, the questions, and the messiness that lies behind clean lines and geometric shapes”

Nelson, Diane in Wernimont, Jaqueline 2021, Quantification.” In Uncertain Archives: Critical Keywords for Big Data

(Bowker & Star 2000, 321)

D’Ignazio, C. & Klein, L. (2020). Chapter 3: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints.

Haraway, Donna in Situated Knowleges (1988)