Racial Trauma Unfolds: The Spectacle of Witnessing George Floyd`s Murder
Bibliographic record
Abstract
Abstract In this article, we chart some of the detrimental emotional impacts of what we consider a human disaster: denying Black people’s humanity. We focus on the highly publicized and violent killing of George Floyd by Minneapolis police officers in 2020: 9 minutes and 29 seconds of state-sanctioned, anti-Black violence that was filmed and circulated globally and has sparked the largest racial justice protest and beyond since the civil rights movements of the 1960s. We consider the impacts of viewing this footage, this spectacle, on Black people, seeing this human disaster playing out in front of their eyes through the lens of anti-Black racism (ABR), which serves as an analytic lens to theorize this trauma within the context of visceral and ubiquitous anti-Black racism. We further contextualize these links between racism and trauma by drawing from our firsthand experience, as well as the stories, worries, and feelings shared with us by Black professionals, families, and members of the community. We focus specifically on that shared by Black youth, which has primarily been the focus of our professional work. We conclude by highlighting strategies of resistance to counteract these impacts, as well as shifts to clinical practice that might better address them and structural shifts towards social justice. Keywords: Human-made disasters, mental health, trauma, anti-Black racism, resistance, resilience.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".