Seeing Race Before Race: Visual Culture and the Racial Matrix in the Premodern World
Noémie Ndiaye, Lia Markey
Explores the deployment of racial thinking and racial formations in the visual culture of the pre-modern world. The capacious visual archive studied in this volume includes a trove of materials such as annotated or illuminated manuscripts, Renaissance costume books and travel books, maps and cartographic volumes produced by Europeans as well as Indigenous peoples, mass-printed pamphlets, jewelry, decorative arts, religious iconography, paintings from around the world, ceremonial objects, festival books, and play texts intended for live performance. Contributors explore the deployment of what coeditor Noémie Ndiaye calls "the racial matrix" and its interconnected paradigms across the medieval and early modern chronological divide and across vast transnational and multilingual geographies. This volume uses items from the Fall 2023 exhibition "Seeing Race Before Race"--a collaboration between RaceB4Race and the Newberry Library--as a starting point for an ambitious theoretical conversation between premodern race studies, art history, performance studies, book history, and critical race theory.
Award History
1 wins · 1 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| PROSE Award for Art History and Criticism | 2024 | Winner | Official source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
None extracted with sufficient confidence.
02 Central places
None extracted with sufficient confidence.
03 Suggested argument
Racial thinking and racial formations were deployed through the pre-modern world’s visual culture, connected via an interlocking “racial matrix” across regions and the medieval-to-early-modern divide.
Model confidence 72%
04 Reading orientation
65 / 100 academicAcademic
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 62%.