Incident characteristics of fatal forcible entry warrant raids in the USA (2010–6)
Bibliographic record
Abstract
Abstract Following the police killings of Breonna Taylor, Amir Locke, and others, forcible entry warrant raids (FEWRs) by law enforcement have become especially controversial in the USA. Despite the growing debate over this law enforcement practice, there is little available data, and consequently, a lack of empirical research related to FEWRs. The current study utilizes a nationwide public database of fatal FEWR incidents in the USA from 2010 to 2016 to examine nationwide trends of fatal FEWRs, civilian and officer characteristics, warrant characteristics, situational characteristics, and the outcomes of investigations and civil lawsuits following fatal FEWRs. Results suggest that, while fatal FEWRs were common between 2010 and 2016, Black and African American civilians were overrepresented in the data. Results suggest that fatal FEWRs most frequently occurred during the execution of drug warrants and about one-quarter resulted in the death of a civilian who was not armed with a firearm. Most of these cases resulted in no charges filed against the officers, but approximately one-quarter resulted in a civil lawsuit. This examination advances the limited research on FEWRs, providing greater detail on the common incident characteristics of these raids, along with the individuals and communities that are frequently impacted by them.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".