The Limits of Deadly Force Databases for Studying Lethal Force by Police
Bibliographic record
Abstract
Although advocates have long been calling for comprehensive databases of deadly force by police, relatively little progress has been made. Given this, various bodies have stepped in to fill this gap (for example, the Canadian Broadcasting Corporation). While these efforts have been useful in providing the public with important information about fatal police shootings, existing databases are limited in various ways, especially when used for research purposes. For example, they exclude most police shootings and present a relatively small, non-random sample of situations where an officer discharges their firearm. Evidence suggests that fatal police shootings are not evenly distributed across jurisdictions, but the likelihood of mortality is explained by a range of factors, such as proximity to trauma centres, which leads to geographic variations in fatal shootings. Despite these types of limitations, researchers use these databases to study and make statements about police shootings, including how various reform efforts have influenced lethal force by police. The chapter discusses some of the limitations associated with existing deadly force databases and describes their implications for use-of-force research. Recommendations are presented for researchers who choose to use these sorts of databases for research. In the final section of the chapter, calls for a concerted effort to develop more comprehensive use-of-force databases and describes what they should include. Capturing all police shootings regardless of outcome, would provide a better understanding of the number of times officers discharge their firearm, and also minimize the impact of other limitations that characterize current deadly force databases.
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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 | 0.122 | 0.413 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.031 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".