The wounds that do not heal: Black expendability and the traumatizing aftereffects of anti-Black police violence
Bibliographic record
Abstract
Purpose The murders of Black people at the hands of police in 2020 have led to global protests that have called on public officials to defund or abolish the police. What has been drowned out in these conversations, however, is the traumatizing aftereffects of anti-Black police violence as a public health crisis. In this paper, I argue that the racial terrorism of anti-Black police violence is a deeply felt wound in Black communities that extends beyond the individuals who directly experience it and that this type of collective trauma must be understood as an urgent public health crisis. Design/methodology/approach Using published studies and online commentaries on anti-Black police violence and its mental health impacts in Canada and the United States, this paper examines the mental health impacts of anti-Black police violence at both the individual and community levels. Findings A public health response to the traumatizing aftereffects of anti-Black police violence and other forms of state violence must highlight important policy imperatives, such as policies of action focused on improving the public health system. It must also encompass a recognition that the public health crisis of anti-Black police violence is not solvable solely by public health agencies alone. Rather, strategic opportunities to address this crisis arise at every level of governmental interaction, including law enforcement, health care, employment, business, education and the media. Originality/value While the impact of anti-Black police violence on the mental health of Black individuals has been emerging in the literature over the last several years, what has been less focused on and what I address in this paper is how the threat of that violence lingers in Black communities long after the protestors have packed up their megaphones, resulting in collective trauma in Black communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".