Multi-group data versus dual-side theory: On race contrasts and police-caused homicides
Bibliographic record
Abstract
Empirical evidence points to a persistent Black-White racial gap in police-caused homicides. Some scholarship treats the gap as denoting criminal justice exposure either in terms of involvement in crime or living in a high-crime context. By contrast, health scholarship typically points to the importance of racism including the attitudes, institutional practices, and overall structures that operate to privilege one group over another. Still, given the demographics of US society, the Black-White racial contrast overlooks the 25% of Americans who are neither Black nor White: Native Americans, Latinos, and Asians. The question of how the groups should be organized vis-a-vis the current Black-White model and theories arises. An answer is not straightforward. There is a rank-ordering to the groups' mortality rates as well as an exponential increase in the number of possible comparisons. In this paper we systematically review the literature on race and police-caused homicide with a particular focus on studies that attempt to move beyond the Black-White model. We find that studies on race and police-caused homicide either make no comparison between the groups, or, alternatively, use a White-non-White, a Black-non-Black, and/or a Black-Native American-Latino vs. White-Asian comparison. We use data on group-specific mortality rates to examine the strengths and limits of each of these practices. The limits are the selection of counterfactual gaps, the selection of smaller gaps, and/or the omission of larger gaps. To address these limits, we propose that a Black-Native American vs. Latino-White-Asian model best captures the higher and lower mortality rates in police-caused homicide data.
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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 | 0.104 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".