Dis-Placing White Supremacy: Intersections of Black and Indigenous Struggles in the Removal of the Roosevelt Statue at the American Museum of Natural History
Bibliographic record
Abstract
The removal of monuments and the renaming of places have become major flashpoints of social and political contestation over the past decade. In the United States, there has been a surge in the removal of neo-Confederate statues, monuments and place names as well as a dethroning of statues of Christopher Columbus and other prominent figures who organised and committed Indigenous genocide. The iconoclasms of these two movements—for the removal of anti-Black and anti-Indigenous monuments—are often portrayed as separate struggles. However, this clean delineation of the commemorative landscape between those features that embody anti-Blackness and anti-Indigeneity obscures how both are informed by the ideology of white supremacy and emplace it in the built environment. Campaigns for the removal of monumental objects such as the Equestrian Statue of Theodore Roosevelt outside the American Museum of Natural History in New York demonstrate the intersectionality of commemorative struggles and the potential for solidarity between Black and Indigenous peoples. While recognising the distinct struggles of Black and Indigenous communities, this chapter argues that the intimacies, resonances and collaborations to dismantle white supremacy and settler-colonial monumentality can open spaces to nurture the possibilities for intersectional solidarity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.028 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".