Toppled Monuments and Black Lives Matter: Race, Gender, and Decolonization in the Public Space. An Interview with Charmaine A. Nelson
Bibliographic record
Abstract
This paper discusses the recent backlash against public monuments spurred by Black Lives Matter (BLM) protests in North America and elsewhere following the killing by police of George Floyd, an unarmed African-American man in the United States. Since this event, protestors have taken to the streets to bring attention to police brutality, systemic racism, and racial injustice faced by Black and Indigenous people and people of colour in the United States, Canada, Great Britain and some European countries. In many of these protests, outraged citizens have torn down, toppled, or defaced monuments of well-known historic figures associated with colonialism, slavery, racism, and imperialism. Protestors have been demanding the removal of statues and monuments that symbolize slavery, colonial power, and systemic and historical racism. What makes these monuments problematic and what drives these deliberate and spectacular acts of defiance against these omnipresent monuments? Featuring an interview with art historian Charmaine A. Nelson, this article explores the meanings of these forceful, decolonial articulations at this moment. The interview addresses some complex questions related to monumentalization and the public sphere, symbolism and racial in/justice. In so doing, it suggests that monuments of the future need to be reimagined and redefined contemporaneously with shifting social knowledge and generational change.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.029 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".