From Susceptibility to Immunity: The Pandemic of Homicide Grief
Bibliographic record
Abstract
Grief from homicide is a global phenomenon. A pattern of structural racism and systemic inequities have shaped Black homicide deaths and have increased the prevalence, susceptibility, spread, and impact of homicide grief for Black communities throughout the global diaspora. A complex interplay of structural vulnerabilities has constituted a worldwide pandemic of homicide grief for Black communities. In this paper, homicide grief is conceptualized as a pandemic. Historical and structural factors that create vulnerability to homicide loss, enhance susceptibility, and facilitate the spread of homicide grief for Black communities is described. A public health framework incorporating prevention, protection, and mitigation strategies is critical to addressing pandemics. A public health framework that incorporates anti-Black racism and other sociopolitical factors that render Black communities disproportionately vulnerable to homicide grief is proposed. Recommendations for reducing the prevalence and interrupting the inequitable spread of homicide grief within Black communities is discussed, including improved access to culturally responsive grief and bereavement services, influencing policy structures, and increasing culturally attuned research efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".