Racial Disparities in Victim Compensation Among Homicide Survivors in the United States
Bibliographic record
Abstract
This study investigates racial disparities in victim compensation among families of homicide victims across 18 U.S. states from 2015 to 2023. Using individual-level administrative data and national expenditure records, we find that families of Black homicide victims file more claims than any other group but have lower approval rates than families of White victims. We document statistically significant racial disparities in claim approvals, even after controlling for gender, state, and application year. The disparity is especially pronounced in denials attributed to “contributory misconduct,” a discretionary determination made largely by law enforcement assessing whether the deceased was engaged in conduct, broadly defined, that may have contributed to their victimization. Contributory misconduct accounted for nearly one-third of all denials, and families of Black victims comprised 57% of such denials despite accounting for only 46% of claims. Our findings show that this mechanism, alongside others such as incomplete applications and statutory ineligibility, disproportionately impacts Black families. These patterns suggest that discretionary and potentially racially biased judgments embedded in the compensation process can exacerbate structural inequalities in access to victim services. Our findings support recent but withdrawn proposals to revise the federal Victims of Crime Act Compensation Guidelines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".